143-773: Swarm behaviour , or swarming , is a collective behaviour exhibited by entities, particularly animals, of similar size which aggregate together, perhaps milling about the same spot or perhaps moving en masse or migrating in some direction. It is a highly interdisciplinary topic. As a term, swarming is applied particularly to insects, but can also be applied to any other entity or animal that exhibits swarm behaviour. The term flocking or murmuration can refer specifically to swarm behaviour in birds, herding to refer to swarm behaviour in tetrapods , and shoaling or schooling to refer to swarm behaviour in fish. Phytoplankton also gather in huge swarms called blooms , although these organisms are algae and are not self-propelled
286-400: A bit string . Typically, numeric parameters can be represented by integers , though it is possible to use floating point representations. The floating point representation is natural to evolution strategies and evolutionary programming . The notion of real-valued genetic algorithms has been offered but is really a misnomer because it does not really represent the building block theory that
429-416: A carrion site, where decomposition likely increased soil nutrient levels and host plant quality. Midges, such as Tokunagayusurika akamusi , form swarms, dancing in the air. Swarming serves multiple purposes, including the facilitation of mating by attracting females to approach the swarm, a phenomenon known as lek mating . Such cloud-like swarms often form in early evening when the sun is getting low, at
572-535: A "child" solution using the above methods of crossover and mutation, a new solution is created which typically shares many of the characteristics of its "parents". New parents are selected for each new child, and the process continues until a new population of solutions of appropriate size is generated. Although reproduction methods that are based on the use of two parents are more "biology inspired", some research suggests that more than two "parents" generate higher quality chromosomes. These processes ultimately result in
715-412: A V-formation may conserve 12–20% of the energy they would need to fly alone. Red knots and dunlins were found in radar studies to fly 5 km per hour faster in flocks than when they were flying alone. The birds flying at the tips and at the front are rotated in a timely cyclical fashion to spread flight fatigue equally among the flock members. The formation also makes communication easier and allows
858-527: A deeper understanding of the behaviour. Early studies of swarm behaviour employed mathematical models to simulate and understand the behaviour. The simplest mathematical models of animal swarms generally represent individual animals as following three rules: The boids computer program, created by Craig Reynolds in 1986, simulates swarm behaviour following the above rules. Many subsequent and current models use variations on these rules, often implementing them by means of concentric "zones" around each animal. In
1001-513: A floating point representation. An expansion of the Genetic Algorithm accessible problem domain can be obtained through more complex encoding of the solution pools by concatenating several types of heterogenously encoded genes into one chromosome. This particular approach allows for solving optimization problems that require vastly disparate definition domains for the problem parameters. For instance, in problems of cascaded controller tuning,
1144-539: A fluid environment may save energy when swimming or flying together, much in the way that bicyclists may draft one another in a peloton . Geese flying in a Vee formation are also thought to save energy by flying in the updraft of the wingtip vortex generated by the previous animal in the formation. Ducklings have also been shown to save energy by swimming in a line. Increased efficiencies in swimming in groups have also been proposed for schools of fish and Antarctic krill. Another example can be seen in homing pigeons. When
1287-687: A focal animal will align its direction of motion with its neighbors. In the outmost zone of attraction, extending the largest distance from the focal animal as it is able to sense, the focal animal will move towards a neighbor. The shape of these zones is affected by the sensory capabilities of the animal. For example, the visual field of a bird does not extend behind its body. Fish, on the other hand, rely on both vision and on hydrodynamic signals relayed through its lateral line . Antarctic krill rely on vision and on hydrodynamic signals relayed through its antennae . Recent studies of starling flocks have shown, however, that each bird modifies its position relative to
1430-458: A focal animal. Packing Fraction : Packing fraction is a parameter borrowed from physics to define the organization (or state i.e. solid, liquid, or gas) of 3D animal groups. It is an alternative measure to density. In this parameter, the aggregation is idealized as an ensemble of solid spheres, with each animal at the center of a sphere. The packing fraction is defined as the ratio of the total volume occupied by all individual spheres divided by
1573-431: A general rule of thumb genetic algorithms might be useful in problem domains that have a complex fitness landscape as mixing, i.e., mutation in combination with crossover , is designed to move the population away from local optima that a traditional hill climbing algorithm might get stuck in. Observe that commonly used crossover operators cannot change any uniform population. Mutation alone can provide ergodicity of
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#17328524759361716-451: A genetic algorithm, a population of candidate solutions (called individuals, creatures, organisms, or phenotypes ) to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties (its chromosomes or genotype ) which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from
1859-694: A higher risk of epidemics. This is particularly due to the large amount of waste material produced by larger groups, allowing for a favourable environment for pathogens to thrive. Another cost to group living is the competition over food resources. As individuals group together, there is an increased nutritional requirement of the larger group compared to smaller groups. This causes an increased energetic cost as individuals now travel farther to visit resource patches. An example of intraspecific competition can be seen within groups of whales and dolphins. Female bottle-nose dolphins with similar home ranges tend to have varied foraging habits in an effort to reduce and negate
2002-470: A homing pigeon is released with other individuals from its roost, these pigeon groups showed increased efficiency and decision making to shorten the distance of the route taken to return home, thus saving energy when flying between locations. Animals that form colonies form a cost of living in groups. These colonies exhibit a system with close physical proximity and increased contact between individuals, thus increasing transmission of disease and ectoparasites;
2145-473: A less optimal solution. This generational process is repeated until a termination condition has been reached. Common terminating conditions are: Genetic algorithms are simple to implement, but their behavior is difficult to understand. In particular, it is difficult to understand why these algorithms frequently succeed at generating solutions of high fitness when applied to practical problems. The building block hypothesis (BBH) consists of: Goldberg describes
2288-506: A loss of alignment in the group appears to increase the randomness of its motion, until an aligned state is again achieved. This noise-induced alignment appears to be an intrinsic characteristic of collective coherent motion. Insect migration is the seasonal movement of insects , particularly those by species of dragonflies , beetles , butterflies , and moths . The distance can vary from species to species, but in most cases these movements involve large numbers of individuals. In some cases
2431-430: A more detrimental effect on smaller, isolated groups of individuals, as they are at a greater risk of inbreeding and thus suppressing the group’s overall fitness. The structure of large animal groups has been difficult to study because of the large number of animals involved. The experimental approach is therefore often complemented by mathematical modeling of animal aggregations. The purpose of experiments investigating
2574-488: A neighbour. The shape of these zones will necessarily be affected by the sensory capabilities of a given animal. For example, the visual field of a bird does not extend behind its body. Fish rely on both vision and on hydrodynamic perceptions relayed through their lateral lines , while Antarctic krill rely both on vision and hydrodynamic signals relayed through antennae . However recent studies of starling flocks have shown that each bird modifies its position, relative to
2717-557: A next action, by the same or a different agent. In that way, subsequent actions tend to reinforce and build on each other, leading to the spontaneous emergence of coherent, apparently systematic activity. Stigmergy is a form of self-organization. It produces complex, seemingly intelligent structures, without need for any planning, control, or even direct communication between the agents. As such it supports efficient collaboration between extremely simple agents, who lack any memory, intelligence or even awareness of each other. Swarm intelligence
2860-543: A number of aspects of flock behaviour. Aggregations of animals are faced with decisions which they must make if they are to remain together. For a school of fish, an example of a typical decision might be which direction to swim when confronted by a predator. Social insects such as ants and bees must collectively decide where to build a new nest. A herd of elephants must decide when and where to migrate. How are these decisions made? Do stronger or more experienced 'leaders' exert more influence than other group members, or does
3003-426: A number of aspects of flock behaviour. In order to gain insight into why animals evolve swarming behaviours, scientists have turned to evolutionary models that simulate populations of evolving animals. Typically these studies use a genetic algorithm to simulate evolution over many generations. These studies have investigated a number of hypotheses attempting to explain why animals evolve swarming behaviours, such as
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#17328524759363146-444: A number of steps from maternal DNA adding a number of steps from paternal DNA and so on. This is like adding vectors that more probably may follow a ridge in the phenotypic landscape. Thus, the efficiency of the process may be increased by many orders of magnitude. Moreover, the inversion operator has the opportunity to place steps in consecutive order or any other suitable order in favour of survival or efficiency. A variation, where
3289-441: A population of randomly generated individuals, and is an iterative process , with the population in each iteration called a generation . In each generation, the fitness of every individual in the population is evaluated; the fitness is usually the value of the objective function in the optimization problem being solved. The more fit individuals are stochastically selected from the current population, and each individual's genome
3432-463: A population on each of the computer nodes and migration of individuals among the nodes. Fine-grained parallel genetic algorithms assume an individual on each processor node which acts with neighboring individuals for selection and reproduction. Other variants, like genetic algorithms for online optimization problems, introduce time-dependence or noise in the fitness function. Genetic algorithms with adaptive parameters (adaptive genetic algorithms, AGAs)
3575-418: A predator is less likely to chance upon a single group than a scattered distribution. In the attack component, it was thought that an attacking predator is less likely to eat a particular animal when a greater number of individuals are present. In sum, an individual has an advantage if it is in the larger of two groups, assuming that the probability of detection and attack does not increase disproportionately with
3718-455: A reproductive function since they provide increased access to potential mates. Some scientists have provided disadvantages to mating in aggregations by using robotic male crabs; a female is at a higher risk approaching a cluster, has the ability of comparing males, increasing mate competition. Several anti-predator functions of animal aggregations have been proposed. One potential method by which fish schools or bird flocks may thwart predators
3861-812: A set of tiny robots that appear to the roaches as other roaches and can thus alter the roaches' perception of critical mass . The robots were also specially scented so that they would be accepted by the real roaches. Locusts are the swarming phase of the short-horned grasshoppers of the family Acrididae . Some species can breed rapidly under suitable conditions and subsequently become gregarious and migratory. They form bands as nymphs and swarms as adults—both of which can travel great distances, rapidly stripping fields and greatly damaging crops . The largest swarms can cover hundreds of square miles and contain billions of locusts. A locust can eat its own weight (about 2 grams) in plants every day. That means one million locusts can eat more than one tonne of food each day, and
4004-491: A simple quorum rule such that individuals watched the decisions of others before making their own decisions. This technique generally resulted in the 'correct' decision but occasionally cascaded into the 'incorrect' decision. In addition, as the group size increased, the fish made more accurate decisions in following the more attractive fish model. Consensus decision-making, a form of collective intelligence , thus effectively uses information from multiple sources to generally reach
4147-485: A simple set of individual interactions. Cockroaches are mainly nocturnal and will run away when exposed to light. A study tested the hypothesis that cockroaches use just two pieces of information to decide where to go under those conditions: how dark it is and how many other cockroaches there are. The study conducted by José Halloy and colleagues at the Free University of Brussels and other European institutions created
4290-481: A source of fascination for children, naturalists and artists. Individual insects seem to do their own thing without any central control, yet the colony as a whole behaves in a highly coordinated manner. Researchers have found that cooperation at the colony level is largely self-organized . The group coordination that emerges is often just a consequence of the way individuals in the colony interact. These interactions can be remarkably simple, such as one ant merely following
4433-504: A study conducted on groups of leaf monkeys show that infant monkeys in larger group sizes developed slower than those in smaller group sizes. This staggered infant development in the larger groups were closely related to the reduced energetic gain of mothers with reduced available nutrition, thus negatively affecting infant developmental rates. It was also shown that females within the larger groups reproduced more slowly compared to females in smaller groups. The Eurasian badger ( Meles meles )
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4576-519: A universal hazard of animals living in groups. For example, cliff swallows that are commonly parasitized by swallow bugs incur a cost when forming colonies, as these parasitic bugs increase the mortality rates of cliff swallow nestlings. A study shows that the number of swallow bugs found in cliff swallow nests increased with the increase of cliff swallow colony size, thus reducing overall success of these colonies. Larger groups of animals tend to harbour an increased number of pathogens and are at
4719-416: Is a form of social behavior involving the coordinated behavior of large groups of similar animals as well as emergent properties of these groups. This can include the costs and benefits of group membership, the transfer of information, decision-making process, locomotion and synchronization of the group. Studying the principles of collective animal behavior has relevance to human engineering problems through
4862-413: Is also a scientific stream attempting to model the swarm systems themselves and understand their underlying mechanisms, and an engineering stream focused on applying the insights developed by the scientific stream to solve practical problems in other areas. Swarm algorithms follow a Lagrangian approach or an Eulerian approach. The Eulerian approach views the swarm as a field , working with the density of
5005-409: Is also studied by active matter physicists as a phenomenon which is not in thermodynamic equilibrium , and as such requires the development of tools beyond those available from the statistical physics of systems in thermodynamic equilibrium. In this regard, swarming has been compared to the mathematics of superfluids , specifically in the context of starling flocks (murmuration). Swarm behaviour
5148-449: Is an example of a species that incur a cost of group living on the successful reproductive rates. Females present in larger groups of badgers have an increased reproductive failure rate compared to solitary badgers. This is a result of increased reproductive competition within the female individuals in the group. Another cost to group living is stress levels within individuals of a group. Stress levels within group living varies dependent on
5291-451: Is an important regulator of reef fish groups after the initial benefits of refuge grouping and predatory protection. Interesting contrasts to the benefit of increased group size on foraging efficiency can be seen in nature particularly due to intraspecific interactions. A study conducted on the Alaskan moose shows that with increasing group size, there is a decrease in foraging efficiency. This
5434-421: Is another significant and promising variant of genetic algorithms. The probabilities of crossover (pc) and mutation (pm) greatly determine the degree of solution accuracy and the convergence speed that genetic algorithms can obtain. Researchers have analyzed GA convergence analytically. Instead of using fixed values of pc and pm , AGAs utilize the population information in each generation and adaptively adjust
5577-406: Is as an array of bits (also called bit set or bit string ). Arrays of other types and structures can be used in essentially the same way. The main property that makes these genetic representations convenient is that their parts are easily aligned due to their fixed size, which facilitates simple crossover operations. Variable length representations may also be used, but crossover implementation
5720-495: Is employed. An adequate population size ensures sufficient genetic diversity for the problem at hand, but can lead to a waste of computational resources if set to a value larger than required. In addition to the main operators above, other heuristics may be employed to make the calculation faster or more robust. The speciation heuristic penalizes crossover between candidate solutions that are too similar; this encourages population diversity and helps prevent premature convergence to
5863-431: Is how a fish can choose to join a shoal of animals similar to themselves, given that it cannot know its own appearance. Experiments with zebrafish have shown that shoal preference is a learned ability, not innate. A zebrafish tends to associate with shoals that resemble shoals in which it was reared, a form of imprinting . Other open questions of shoaling behaviour include identifying which individuals are responsible for
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6006-493: Is made by Poli. Researchers in Switzerland have developed an algorithm based on Hamilton's rule of kin selection. The algorithm shows how altruism in a swarm of entities can, over time, evolve and result in more effective swarm behaviour. The earliest evidence of swarm behaviour in animals dates back about 480 million years. Fossils of the trilobite Ampyx priscus have been recently described as clustered in lines along
6149-441: Is modified ( recombined and possibly randomly mutated) to form a new generation. The new generation of candidate solutions is then used in the next iteration of the algorithm . Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. A typical genetic algorithm requires: A standard representation of each candidate solution
6292-440: Is more complex in this case. Tree-like representations are explored in genetic programming and graph-form representations are explored in evolutionary programming ; a mix of both linear chromosomes and trees is explored in gene expression programming . Once the genetic representation and the fitness function are defined, a GA proceeds to initialize a population of solutions and then to improve it through repetitive application of
6435-433: Is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of intelligent global behaviour, unknown to the individual agents. Swarm intelligence research is multidisciplinary. It can be divided into natural swarm research studying biological systems and artificial swarm research studying human artefacts. There
6578-460: Is often employed. In this way, small changes in the integer can be readily affected through mutations or crossovers. This has been found to help prevent premature convergence at so-called Hamming walls , in which too many simultaneous mutations (or crossover events) must occur in order to change the chromosome to a better solution. Other approaches involve using arrays of real-valued numbers instead of bit strings to represent chromosomes. Results from
6721-474: Is quite unnatural to model applications in terms of genetic operators like mutation and crossover on bit strings. The pseudobiology adds another level of complexity between you and your problem. Second, genetic algorithms take a very long time on nontrivial problems. [...] [T]he analogy with evolution—where significant progress require [sic] millions of years—can be quite appropriate. [...] I have never encountered any problem where genetic algorithms seemed to me
6864-413: Is result of increased social aggression in the groups, as the individuals of the group spent most of its time in alert-alarm postures, thus spending less time foraging and feeding, reducing its foraging efficiency. With increasing colony size and competition of resources within individuals of a group, reproductive rates and development of offspring may vary due to reduced resource availability. For example,
7007-500: Is the collective behaviour of decentralized , self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence . The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems. Swarm intelligence systems are typically made up of a population of simple agents such as boids interacting locally with one another and with their environment. The agents follow very simple rules, and although there
7150-446: Is the " encounter dilution " effect. Hamilton, for instance, proposed that the aggregation of animals was due to a "selfish" avoidance of a predator and was thus a form of cover-seeking. Another formulation of the theory was given by Turner and Pitcher and was viewed as a combination of detection and attack probabilities. In the detection component of the theory, it was suggested that potential prey might benefit by living together since
7293-399: Is the "many eyes" hypothesis. This theory states that as the size of the group increases, the task of scanning the environment for predators can be spread out over many individuals. Not only does this mass collaboration presumably provide a higher level of vigilance, it could also allow more time for individual feeding. A third hypothesis for an anti-predatory effect of animal aggregation
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#17328524759367436-399: Is the sum of values of all objects in the knapsack if the representation is valid, or 0 otherwise. In some problems, it is hard or even impossible to define the fitness expression; in these cases, a simulation may be used to determine the fitness function value of a phenotype (e.g. computational fluid dynamics is used to determine the air resistance of a vehicle whose shape is encoded as
7579-466: Is the ‘predator confusion effect’ proposed and demonstrated by Milinski and Heller (1978). This theory is based on the idea that it becomes difficult for predators to pick out individual prey from groups because the many moving targets create a sensory overload of the predator's visual channel. Milinski and Heller's findings have been corroborated both in experiment and computer simulations. A second potential anti-predator effect of animal aggregations
7722-446: Is typically a kilometre or more from the original hive, though some species, e.g., Apis dorsata , may establish new colonies within as little as 500 meters from the natal nest. This collective decision-making process is remarkably successful in identifying the most suitable new nest site and keeping the swarm intact. A good hive site has to be large enough to accommodate the swarm (about 15 litres in volume), has to be well-protected from
7865-474: Is usually used in physics to characterize the degree of spatial order in a system of particles. It also describes the density, but this measures describes the density at a distance away from a given point. Cavagna et al. found that flocks of starlings exhibited more structure than a gas but less than a liquid. The simplest mathematical models of animal aggregations generally instruct the individual animals to follow three rules: Two examples of this simulation are
8008-457: Is worth tuning parameters such as the mutation probability, crossover probability and population size to find reasonable settings for the problem class being worked on. A very small mutation rate may lead to genetic drift (which is non- ergodic in nature). A recombination rate that is too high may lead to premature convergence of the genetic algorithm. A mutation rate that is too high may lead to loss of good solutions, unless elitist selection
8151-548: The Boids program created by Craig Reynolds in 1986 and the Self Propelled Particle model. Many current models use variations on these rules. For instance, many models implement these three rules through layered zones around each animal. In the zone of repulsion very close to the animal, the focal animal will seek to distance itself from its neighbors in order to avoid a collision. In the slightly further away zone of alignment,
8294-608: The Bosphorus at migration times. More common species, such as the European honey buzzard , can be counted in hundreds of thousands in autumn. Other barriers, such as mountain ranges, can also cause funnelling, particularly of large diurnal migrants. This is a notable factor in the Central American migratory bottleneck. This concentration of birds during migration can put species at risk. Some spectacular migrants have already gone extinct,
8437-409: The pc and pm in order to maintain the population diversity as well as to sustain the convergence capacity. In AGA (adaptive genetic algorithm), the adjustment of pc and pm depends on the fitness values of the solutions. There are more examples of AGA variants: Successive zooming method is an early example of improving convergence. In CAGA (clustering-based adaptive genetic algorithm), through
8580-477: The selfish herd theory the predator confusion effect, the dilution effect, and the many eyes theory. The concept of emergence—that the properties and functions found at a hierarchical level are not present and are irrelevant at the lower levels–is often a basic principle behind self-organizing systems . An example of self-organization in biology leading to emergence in the natural world occurs in ant colonies. The queen does not give direct orders and does not tell
8723-422: The "zone of repulsion", very close to the animal, the focal animal will seek to distance itself from its neighbours to avoid collision. Slightly further away, in the "zone of alignment", the focal animal will seek to align its direction of motion with its neighbours. In the outermost "zone of attraction", which extends as far away from the focal animal as it is able to sense, the focal animal will seek to move towards
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#17328524759368866-417: The 1960s and early 1970s – Rechenberg's group was able to solve complex engineering problems through evolution strategies . Another approach was the evolutionary programming technique of Lawrence J. Fogel , which was proposed for generating artificial intelligence. Evolutionary programming originally used finite state machines for predicting environments, and used variation and selection to optimize
9009-556: The Building Block Hypothesis in adaptively reducing disruptive recombination. Prominent examples of this approach include the mGA, GEMGA and LLGA. Problems which appear to be particularly appropriate for solution by genetic algorithms include timetabling and scheduling problems , and many scheduling software packages are based on GAs . GAs have also been applied to engineering . Genetic algorithms are often applied as an approach to solve global optimization problems. As
9152-457: The Eulerian approach. Ant colony optimization is a widely used algorithm which was inspired by the behaviours of ants, and has been effective solving discrete optimization problems related to swarming. The algorithm was initially proposed by Marco Dorigo in 1992, and has since been diversified to solve a wider class of numerical problems. Species that have multiple queens may have a queen leaving
9295-402: The ability to solve geometric problems. For example, colonies routinely find the maximum distance from all colony entrances to dispose of dead bodies. A further key concept in the field of swarm intelligence is stigmergy . Stigmergy is a mechanism of indirect coordination between agents or actions. The principle is that the trace left in the environment by an action stimulates the performance of
9438-494: The amount of time necessary for larger groups to find food was established. Further support for an enhanced foraging capability of schools is seen in the structure of schools of predatory fish. Partridge and others analyzed the school structure of Atlantic bluefin tuna from aerial photographs and found that the school assumed a parabolic shape, a fact that was suggestive of cooperative hunting in this species (Partridge et al., 1983). This theory states that groups of animals moving in
9581-414: The animal nearest to the focal animal. This parameter can be found for each animal in an aggregation and then averaged. Care must be taken to account for the animals located at the edge of an animal aggregation. These animals have no neighbor in one direction. Nearest Neighbor Position : In a polar coordinate system, the nearest neighbor position describes the angle and distance of the nearest neighbor to
9724-504: The ants what to do. Instead, each ant reacts to stimuli in the form of chemical scents from larvae, other ants, intruders, food and buildup of waste, and leaves behind a chemical trail, which, in turn, provides a stimulus to other ants. Here each ant is an autonomous unit that reacts depending only on its local environment and the genetically encoded rules for its variety. Despite the lack of centralized decision making, ant colonies exhibit complex behaviours and have even been able to demonstrate
9867-454: The average direction of motion of the other particles in their local neighbourhood. Simulations demonstrate that a suitable "nearest neighbour rule" eventually results in all the particles swarming together, or moving in the same direction. This emerges, even though there is no centralized coordination, and even though the neighbours for each particle constantly change over time. SPP models predict that swarming animals share certain properties at
10010-414: The average orientation of all animals in the group is determined. For each animal, the angular difference between its orientation and the group orientation is then found. The group polarity is then the average of these differences (Viscido 2004). Nearest Neighbor Distance : The nearest neighbor distance (NND) describes the distance between the centroid of one animal (the focal animal) and the centroid of
10153-401: The best solutions. The solutions it finds are called particles . Each particle stores its position as well as the best solution it has achieved so far. The particle swarm optimizer tracks the best local value obtained so far by any particle in the local neighbourhood. The remaining particles then move through the problem space following the lead of the optimum particles. At each time iteration,
10296-445: The best, or closest, food source from several in the vicinity. Such collective decisions are achieved using positive feedback mechanisms. Selection of the best food source is achieved by ants following two simple rules. First, ants which find food return to the nest depositing a pheromone chemical. More pheromone is laid for higher quality food sources. Thus, if two equidistant food sources of different qualities are found simultaneously,
10439-459: The birds to maintain visual contact with each other. Other animals may use similar drafting techniques when migrating. Lobsters , for example, migrate in close single-file formation "lobster trains", sometimes for hundreds of miles. The Mediterranean and other seas present a major obstacle to soaring birds, which must cross at the narrowest points. Massive numbers of large raptors and storks pass through areas such as Gibraltar , Falsterbo , and
10582-707: The chance of capture), enhanced foraging success, and higher success in finding a mate. It is also likely that fish benefit from shoal membership through increased hydrodynamic efficiency. Fish use many traits to choose shoalmates. Generally they prefer larger shoals, shoalmates of their own species, shoalmates similar in size and appearance to themselves, healthy fish, and kin (when recognised). The "oddity effect" posits that any shoal member that stands out in appearance will be preferentially targeted by predators. This may explain why fish prefer to shoal with individuals that resemble them. The oddity effect would thus tend to homogenise shoals. One puzzling aspect of shoal selection
10725-537: The cluster. If a scout finds a suitable location, she returns to the cluster and promotes it by dancing a version of the waggle dance . This dance conveys information about the quality, direction, and distance of the new site. The more excited she is about her findings, the more vigorously she dances. If she can convince others they may take off and check the site she found. If they approve they may promote it as well. In this decision-making process, scouts check several sites, often abandoning their own original site to promote
10868-826: The correct conclusion. Some simulations of collective decision-making use the Condorcet method to model the way groups of animals come to consensus. Genetic algorithm In computer science and operations research , a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems via biologically inspired operators such as selection , crossover , and mutation . Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles , hyperparameter optimization , and causal inference . In
11011-663: The direction of shoal movement. In the case of migratory movement, most members of a shoal seem to know where they are going. In the case of foraging behaviour, captive shoals of golden shiner (a kind of minnow ) are led by a small number of experienced individuals who knew when and where food was available. Radakov estimated herring schools in the North Atlantic can occupy up to 4.8 cubic kilometres (1.2 cu mi) with fish densities between 0.5 and 1.0 fish/cubic metre, totalling several billion fish in one school. Collective animal behaviour Collective animal behaviour
11154-424: The distribution of the sampling probability tuned to focus in those areas of greater interest. During each successive generation, a portion of the existing population is selected to reproduce for a new generation. Individual solutions are selected through a fitness-based process, where fitter solutions (as measured by a fitness function ) are typically more likely to be selected. Certain selection methods rate
11297-433: The early 1960s, and the methods were described in books by Fraser and Burnell (1970) and Crosby (1973). Fraser's simulations included all of the essential elements of modern genetic algorithms. In addition, Hans-Joachim Bremermann published a series of papers in the 1960s that also adopted a population of solution to optimization problems, undergoing recombination, mutation, and selection. Bremermann's research also included
11440-454: The elements of modern genetic algorithms. Other noteworthy early pioneers include Richard Friedberg, George Friedman, and Michael Conrad. Many early papers are reprinted by Fogel (1998). Although Barricelli, in work he reported in 1963, had simulated the evolution of ability to play a simple game, artificial evolution only became a widely recognized optimization method as a result of the work of Ingo Rechenberg and Hans-Paul Schwefel in
11583-690: The elements, receive an optimal amount of sunshine, be some height above the ground, have a small entrance and be capable of resisting ant infestation - that is why tree cavities are often selected. Unlike social insects, swarms of non-social insects that have been studied primarily seem to function in contexts such as mating, feeding, predator avoidance, and migration. Moths may exhibit synchronized mating, during which pheromones released by females initiate searching and swarming behavior in males. Males sense pheromones with sensitive antennae and may track females as far as several kilometers away. Swarm mating involves female choice and male competition. Only one male in
11726-417: The fitness of each solution and preferentially select the best solutions. Other methods rate only a random sample of the population, as the former process may be very time-consuming. The fitness function is defined over the genetic representation and measures the quality of the represented solution. The fitness function is always problem-dependent. For instance, in the knapsack problem one wants to maximize
11869-403: The four following categories: social and genetic, anti-predator, enhanced foraging, and increased locomotion efficiency. Support for the social and genetic function of aggregations, especially those formed by fish, can be seen in several aspects of their behavior. For instance, experiments have shown that individual fish removed from a school will have a higher respiratory rate than those found in
12012-476: The general process of constructing a new population is to allow the best organism(s) from the current generation to carry over to the next, unaltered. This strategy is known as elitist selection and guarantees that the solution quality obtained by the GA will not decrease from one generation to the next. Parallel implementations of genetic algorithms come in two flavors. Coarse-grained parallel genetic algorithms assume
12155-453: The generality and/or practicality of the building-block hypothesis as an explanation for GAs' efficiency still remains. Indeed, there is a reasonable amount of work that attempts to understand its limitations from the perspective of estimation of distribution algorithms. The practical use of a genetic algorithm has limitations, especially as compared to alternative optimization algorithms: The simplest algorithm represents each chromosome as
12298-479: The genetic diversity of the subsequent generation of children. Opinion is divided over the importance of crossover versus mutation. There are many references in Fogel (2006) that support the importance of mutation-based search. Although crossover and mutation are known as the main genetic operators, it is possible to use other operators such as regrouping, colonization-extinction, or migration in genetic algorithms. It
12441-459: The global volume of the aggregation (Cavagna 2008). Values range from zero to one, where a small packing fraction represents a dilute system like a gas. Cavagna found that the packing fraction for groups of starlings was 0.012. Integrated Conditional Density : This parameter measures the density at various length scales and therefore describes the homogeneity of density throughout an animal group. Pair Distribution Function : This parameter
12584-419: The group level, regardless of the type of animals in the swarm. Swarming systems give rise to emergent behaviours which occur at many different scales, some of which are both universal and robust. It has become a challenge in theoretical physics to find minimal statistical models that capture these behaviours. Particle swarm optimization is another algorithm widely used to solve problems related to swarms. It
12727-424: The group make a decision by consensus? The answer probably depends on the species. While the role of a leading matriarch in an elephant herd is well known, studies have shown that some animal species use a consensus approach in their collective decision-making process. A recent investigation showed that small groups of fish used consensus decision-making when deciding which fish model to follow. The fish did this by
12870-442: The heuristic as follows: Despite the lack of consensus regarding the validity of the building-block hypothesis, it has been consistently evaluated and used as reference throughout the years. Many estimation of distribution algorithms , for example, have been proposed in an attempt to provide an environment in which the hypothesis would hold. Although good results have been reported for some classes of problems, skepticism concerning
13013-442: The hind legs or, in some species, simply encountering other individuals causes an increase in levels of serotonin. The transformation of the locust to the swarming variety can be induced by several contacts per minute over a four-hour period. Notably, an innate predisposition to aggregate has been found in hatchlings of the desert locust, Schistocerca gregaria , independent of their parental phase. An individual locust's response to
13156-463: The idea that collective systems can be understood from a set of techniques. For example, Nicolis and Prigogine (1977) employed the use of non-linear thermodynamics to help explain similarities between collective systems at different scales. Other studies aim to use physics, mathematics and chemistry to provide frameworks to study collective phenomena. Many functions of animal aggregations have been proposed. These proposed functions may be grouped into
13299-452: The individuals that migrate in one direction may not return and the next generation may instead migrate in the opposite direction. This is a significant difference from bird migration . Monarch butterflies are especially noted for their lengthy annual migration. In North America they make massive southward migrations starting in August until the first frost. A northward migration takes place in
13442-498: The internal loop controller structure can belong to a conventional regulator of three parameters, whereas the external loop could implement a linguistic controller (such as a fuzzy system) which has an inherently different description. This particular form of encoding requires a specialized crossover mechanism that recombines the chromosome by section, and it is a useful tool for the modelling and simulation of complex adaptive systems, especially evolution processes. A practical variant of
13585-532: The intraspecific competition of resources. Benefits of group living on defence from predators is very evident in nature, however in locations of high resource competition poses an effect on the mortality of certain individuals. This can be seen in species of shoaling fish, where the initial aggregation of individuals to a group initially allowed for the protection from predators, however the limiting resources available changes over time, and mortality rates of these fish begin to increase, showing that resource competition
13728-400: The largest swarms can consume over 100,000 tonnes each day. Swarming in locusts has been found to be associated with increased levels of serotonin which causes the locust to change colour, eat much more, become mutually attracted, and breed much more easily. Researchers propose that swarming behaviour is a response to overcrowding and studies have shown that increased tactile stimulation of
13871-499: The last few mutations to find the absolute optimum. Other techniques (such as simple hill climbing ) are quite efficient at finding absolute optimum in a limited region. Alternating GA and hill climbing can improve the efficiency of GA while overcoming the lack of robustness of hill climbing. This means that the rules of genetic variation may have a different meaning in the natural case. For instance – provided that steps are stored in consecutive order – crossing over may sum
14014-444: The lowest level of cortisol (an indicator of stress), while groups with smaller or larger than 10-20 individuals showed an increased level of cortisol production, thus an increased level of stress within the individuals of the larger and smaller groups. Another proposed cost to group living is the cost incurred to avoid inbreeding. Individuals may it be male or females in groups may disperse in an effort to avoid inbreeding. This poses
14157-445: The migration such as predation. Many birds migrate in flocks. For larger birds, it is assumed that flying in flocks reduces energy costs. The V formation is often supposed to boost the efficiency and range of flying birds, particularly over long migratory routes. All the birds except the first fly in the upwash from one of the wingtip vortices of the bird ahead. The upwash assists each bird in supporting its own weight in flight, in
14300-566: The most notable being the passenger pigeon . During migration the flocks were a mile (1.6 km) wide and 300 miles (500 km) long, taking several days to pass and containing up to a billion birds. The term "shoal" can be used to describe any group of fish, including mixed-species groups, while "school" is used for more closely knit groups of the same species swimming in a highly synchronised and polarised manner. Fish derive many benefits from shoaling behaviour including defence against predators (through better predator detection and by diluting
14443-413: The mutation, crossover, inversion and selection operators. The population size depends on the nature of the problem, but typically contains hundreds or thousands of possible solutions. Often, the initial population is generated randomly, allowing the entire range of possible solutions (the search space ). Occasionally, the solutions may be "seeded" in areas where optimal solutions are likely to be found or
14586-434: The nest along with some workers to found a colony at a new site, a process akin to swarming in honeybees . The concept of self-propelled particles (SPP) was introduced in 1995 by Tamás Vicsek et al. as a special case of the boids model introduced in 1986 by Reynolds. An SPP swarm is modelled by a collection of particles that move with a constant speed and respond to random perturbations by adopting at each time increment
14729-427: The next generation population of chromosomes that is different from the initial generation. Generally, the average fitness will have increased by this procedure for the population, since only the best organisms from the first generation are selected for breeding, along with a small proportion of less fit solutions. These less fit solutions ensure genetic diversity within the genetic pool of the parents and therefore ensure
14872-529: The next generation, known as Holland's Schema Theorem . Research in GAs remained largely theoretical until the mid-1980s, when The First International Conference on Genetic Algorithms was held in Pittsburgh, Pennsylvania . In the late 1980s, General Electric started selling the world's first genetic algorithm product, a mainframe-based toolkit designed for industrial processes. In 1989, Axcelis, Inc. released Evolver ,
15015-763: The ocean floor. The animals were all mature adults, and were all facing the same direction as though they had formed a conga line or a peloton . It has been suggested they line up in this manner to migrate, much as spiny lobsters migrate in single-file queues; it has also been suggested that the formation is the precursor for mating, as with the fly Leptoconops torrens . The findings suggest animal collective behaviour has very early evolutionary origins. Examples of biological swarming are found in bird flocks , fish schools , insect swarms , bacteria swarms , molds, molecular motors , quadruped herds and people. The behaviour of social insects (insects that live in colonies , such as ants, bees, wasps and termites) has always been
15158-439: The overall genetic algorithm process (seen as a Markov chain ). Examples of problems solved by genetic algorithms include: mirrors designed to funnel sunlight to a solar collector, antennae designed to pick up radio signals in space, walking methods for computer figures, optimal design of aerodynamic bodies in complex flowfields In his Algorithm Design Manual , Skiena advises against genetic algorithms for any task: [I]t
15301-532: The particle swarm optimiser accelerates each particle toward its optimum locations according to simple mathematical rules . Particle swarm optimization has been applied in many areas. It has few parameters to adjust, and a version that works well for a specific applications can also work well with minor modifications across a range of related applications. A book by Kennedy and Eberhart describes some philosophical aspects of particle swarm optimization applications and swarm intelligence. An extensive survey of applications
15444-407: The phenotype), or even interactive genetic algorithms are used. The next step is to generate a second generation population of solutions from those selected, through a combination of genetic operators : crossover (also called recombination), and mutation . For each new solution to be produced, a pair of "parent" solutions is selected for breeding from the pool selected previously. By producing
15587-424: The pheromone trail to the better one will be stronger. Ants in the nest follow another simple rule, to favor stronger trails, on average. More ants then follow the stronger trail, so more ants arrive at the high quality food source, and a positive feedback cycle ensures, resulting in a collective decision for the best food source. If there are two paths from the ant nest to a food source, then the colony usually selects
15730-569: The philosophy of biomimetics . For instance, determining the rules by which an individual animal navigates relative to its neighbors in a group can lead to advances in the deployment and control of groups of swimming or flying micro-robots such as UAVs (Unmanned Aerial Vehicles). Examples of collective animal behavior include: The basis of collective animal behaviour originated from the study of collective phenomena; that is, repeated interactions among individuals that produce large scale patterns. The foundation of collective phenomena originates from
15873-457: The population as a whole is evolved rather than its individual members, is known as gene pool recombination. A number of variations have been developed to attempt to improve performance of GAs on problems with a high degree of fitness epistasis, i.e. where the fitness of a solution consists of interacting subsets of its variables. Such algorithms aim to learn (before exploiting) these beneficial phenotypic interactions. As such, they are aligned with
16016-510: The predictive logics. Genetic algorithms in particular became popular through the work of John Holland in the early 1970s, and particularly his book Adaptation in Natural and Artificial Systems (1975). His work originated with studies of cellular automata , conducted by Holland and his students at the University of Michigan . Holland introduced a formalized framework for predicting the quality of
16159-430: The right way to attack it. Further, I have never seen any computational results reported using genetic algorithms that have favorably impressed me. Stick to simulated annealing for your heuristic search voodoo needs. In 1950, Alan Turing proposed a "learning machine" which would parallel the principles of evolution. Computer simulation of evolution started as early as in 1954 with the work of Nils Aall Barricelli , who
16302-604: The same basic behavioural and ecological syndrome, often referred to as "legionary behaviour", and may be an example of convergent evolution . The successful techniques used by ant colonies have been studied in computer science and robotics to produce distributed and fault-tolerant systems for solving problems. This area of biomimetics has led to studies of ant locomotion, search engines that make use of "foraging trails", fault-tolerant storage and networking algorithms . In temperate climates, honey bees usually form swarms in late spring. A swarm typically contains about half
16445-436: The same way a glider can climb or maintain height indefinitely in rising air. Geese flying in a V formation save energy by flying in the updraft of the wingtip vortex generated by the previous animal in the formation. Thus, the birds flying behind do not need to work as hard to achieve lift. Studies show that birds in a V formation place themselves roughly at the optimum distance predicted by simple aerodynamic theory. Geese in
16588-401: The school. This effect has been partly attributed to stress, although hydrodynamic factors were considered more important in this particular study. The calming effect of being with conspecifics may thus provide a social motivation for remaining in an aggregation. Herring, for instance, will become very agitated if they are isolated from conspecifics. Fish schools have also been proposed to serve
16731-467: The shorter path. This is because the ants that first return to the nest from the food source are more likely to be those that took the shorter path. More ants then retrace the shorter path, reinforcing the pheromone trail. Army ants , unlike most ant species, do not construct permanent nests; an army ant colony moves almost incessantly over the time it exists, remaining in an essentially perpetual state of swarming. Several lineages have independently evolved
16874-446: The six or seven animals directly surrounding it, no matter how close or how far away those animals are. Interactions between flocking starlings are thus based on a topological rule rather than a metric rule. It remains to be seen whether the same rule can be applied to other animals. Another recent study, based on an analysis of high speed camera footage of flocks above Rome and assuming minimal behavioural rules, has convincingly simulated
17017-428: The six or seven animals directly surrounding it, no matter how close or how far away those animals are. Interactions between flocking starlings are thus based on a topological , rather than a metric, rule. It remains to be seen whether this applies to other animals. Another recent study, based on an analysis of high-speed camera footage of flocks above Rome and assuming minimal behavioural rules, has convincingly simulated
17160-475: The size of the colony or group. A large group of animals may suffer larger levels of stress arising from intraspecific food competition. In contrast, smaller groups may have increased stress levels arising from the lack of adequate defense from predators as well as a reduced foraging efficiency. An example can be seen in a study conducted on a species of ring-tail lemurs ( Lemur catta ). This study found that an optimum group size of around 10-20 individuals produces
17303-407: The size of the group. A third proposed benefit of animal groups is that of enhanced foraging. This ability was demonstrated by Pitcher and others in their study of foraging behavior in shoaling cyprinids. In this study, the time it took for groups of minnows and goldfish to find a patch of food was quantified. The number of fishes in the groups was varied, and a statistically significant decrease in
17446-566: The spring. How the species manages to return to the same overwintering spots over a gap of several generations is still a subject of research; the flight patterns appear to be inherited, based on a combination of the position of the sun in the sky and a time-compensated Sun compass that depends upon a circadian clock that is based in their antennae. Approximately 1800 of the world's 10,000 bird species are long-distance migrants. The primary motivation for migration appears to be food; for example, some hummingbirds choose not to migrate if fed through
17589-414: The spring. The monarch is the only butterfly that migrates both north and south as the birds do on a regular basis. But no single individual makes the entire round trip. Female monarchs deposit eggs for the next generation during these migrations. The length of these journeys exceeds the normal lifespan of most monarchs, which is less than two months for butterflies born in early summer. The last generation of
17732-449: The structure of animal aggregations is to determine the 3D position of each animal within a volume at each point in time. It is important to know the internal structure of the group because that structure can be related to the proposed motivations for animal grouping. This capability requires the use of multiple cameras trained on the same volume in space, a technique known as stereophotogrammetry . When hundreds or thousands of animals occupy
17875-408: The study volume, it becomes difficult to identify each individual. In addition, animals may block one another in the camera views, a problem known as occlusion. Once the location of each animal at each point in time is known, various parameters describing the animal group can be extracted. These parameters include: Density : The density of an animal aggregation is the number of animals divided by
18018-532: The summer enters into a non-reproductive phase known as diapause and may live seven months or more. During diapause, butterflies fly to one of many overwintering sites. The generation that overwinters generally does not reproduce until it leaves the overwintering site sometime in February and March. It is the second, third and fourth generations that return to their northern locations in the United States and Canada in
18161-503: The superior site of another scout. Several different sites may be promoted by different scouts at first. After some hours and sometimes days, a preferred location eventually emerges from this decision-making process. When all scouts agree on the final location, the whole cluster takes off and swarms to it. Sometimes, if no decision is reached, the swarm will separate, some bees going in one direction; others, going in another. This usually results in failure, with both groups dying. A new location
18304-454: The swarm and deriving mean field properties. It is a hydrodynamic approach, and can be useful for modelling the overall dynamics of large swarms. However, most models work with the Lagrangian approach, which is an agent-based model following the individual agents (points or particles) that make up the swarm. Individual particle models can follow information on heading and spacing that is lost in
18447-516: The swarm—typically the first—will successfully copulate. Females maximize fitness benefits and minimize cost by governing the onset and magnitude of pheromone deployed. Too little pheromone will not attract a mate, too much allows less fit males to sense the signal. After copulation, females lay the eggs on a host plant. Quality of host plant may be a factor influencing the location of swarming and egg-laying. In one case, researchers observed pink-striped oakworm moths ( Anisota virginiensis ) swarming at
18590-436: The theory of schemata suggest that in general the smaller the alphabet, the better the performance, but it was initially surprising to researchers that good results were obtained from using real-valued chromosomes. This was explained as the set of real values in a finite population of chromosomes as forming a virtual alphabet (when selection and recombination are dominant) with a much lower cardinality than would be expected from
18733-957: The tip of a bush, on a hilltop, over a pool of water, or even sometimes above a person. The forming of such swarms is not out of instinct, but an adaptive behavior – a "consensus" – between the individuals within the swarms. It is also suggested that swarming is a ritual , because there is rarely any male midge by itself and not in a swarm. This could have formed due to the benefit of lowering inbreeding by having males of various genes gathering in one spot. The genus Culicoides , also known as biting midges, have displayed swarming behavior which are believed to cause confusion in predators. Cockroaches leave chemical trails in their feces as well as emitting airborne pheromones for mating. Other cockroaches will follow these trails to discover sources of food and water, and also discover where other cockroaches are hiding. Thus, groups of cockroaches can exhibit emergent behaviour , in which group or swarm behaviour emerges from
18876-409: The total value of objects that can be put in a knapsack of some fixed capacity. A representation of a solution might be an array of bits, where each bit represents a different object, and the value of the bit (0 or 1) represents whether or not the object is in the knapsack. Not every such representation is valid, as the size of objects may exceed the capacity of the knapsack. The fitness of the solution
19019-618: The trail left by another ant. Yet put together, the cumulative effect of such behaviours can solve highly complex problems, such as locating the shortest route in a network of possible paths to a food source. The organised behaviour that emerges in this way is sometimes called swarm intelligence , a form of biological emergence . Individual ants do not exhibit complex behaviours, yet a colony of ants collectively achieves complex tasks such as constructing nests, taking care of their young, building bridges and foraging for food. A colony of ants can collectively select (i.e. send most workers towards)
19162-607: The use of clustering analysis to judge the optimization states of the population, the adjustment of pc and pm depends on these optimization states. Recent approaches use more abstract variables for deciding pc and pm . Examples are dominance & co-dominance principles and LIGA (levelized interpolative genetic algorithm), which combines a flexible GA with modified A* search to tackle search space anisotropicity. It can be quite effective to combine GA with other optimization methods. A GA tends to be quite good at finding generally good global solutions, but quite inefficient at finding
19305-438: The volume (or area) occupied by the aggregation. Density may not be a constant throughout the group. For instance, starling flocks have been shown to maintain higher densities on the edges than in the middle of the flock, a feature that is presumably related to defense from predators. Polarity : The group polarity describes if the group animals are all pointing in the same direction or not. In order to determine this parameter,
19448-521: The way animals are. By extension, the term "swarm" is applied also to inanimate entities which exhibit parallel behaviours, as in a robot swarm , an earthquake swarm , or a swarm of stars. From a more abstract point of view, swarm behaviour is the collective motion of a large number of self-propelled entities . From the perspective of the mathematical modeller, it is an emergent behaviour arising from simple rules that are followed by individuals and does not involve any central coordination. Swarm behaviour
19591-453: The winter. Also, the longer days of the northern summer provide extended time for breeding birds to feed their young. This helps diurnal birds to produce larger clutches than related non-migratory species that remain in the tropics. As the days shorten in autumn, the birds return to warmer regions where the available food supply varies little with the season. These advantages offset the high stress, physical exertion costs, and other risks of
19734-400: The workers together with the old queen, while the new queen stays back with the remaining workers in the original hive. When honey bees emerge from a hive to form a swarm, they may gather on a branch of a tree or on a bush only a few meters from the hive. The bees cluster about the queen and send out 20–50 scouts to find suitable new nest locations. The scouts are the most experienced foragers in
19877-596: The world's first commercial GA product for desktop computers. The New York Times technology writer John Markoff wrote about Evolver in 1990, and it remained the only interactive commercial genetic algorithm until 1995. Evolver was sold to Palisade in 1997, translated into several languages, and is currently in its 6th version. Since the 1990s, MATLAB has built in three derivative-free optimization heuristic algorithms (simulated annealing, particle swarm optimization, genetic algorithm) and two direct search algorithms (simplex search, pattern search). Genetic algorithms are
20020-410: Was developed in 1995 by Kennedy and Eberhart and was first aimed at simulating the social behaviour and choreography of bird flocks and fish schools. The algorithm was simplified and it was observed to be performing optimization. The system initially seeds a population with random solutions. It then searches in the problem space through successive generations using stochastic optimization to find
20163-415: Was first simulated on a computer in 1986 with the simulation program boids . This program simulates simple agents (boids) that are allowed to move according to a set of basic rules. The model was originally designed to mimic the flocking behaviour of birds, but it can be applied also to schooling fish and other swarming entities. In recent decades, scientists have turned to modeling swarm behaviour to gain
20306-730: Was proposed by John Henry Holland in the 1970s. This theory is not without support though, based on theoretical and experimental results (see below). The basic algorithm performs crossover and mutation at the bit level. Other variants treat the chromosome as a list of numbers which are indexes into an instruction table, nodes in a linked list , hashes , objects , or any other imaginable data structure . Crossover and mutation are performed so as to respect data element boundaries. For most data types, specific variation operators can be designed. Different chromosomal data types seem to work better or worse for different specific problem domains. When bit-string representations of integers are used, Gray coding
20449-587: Was using the computer at the Institute for Advanced Study in Princeton, New Jersey . His 1954 publication was not widely noticed. Starting in 1957, the Australian quantitative geneticist Alex Fraser published a series of papers on simulation of artificial selection of organisms with multiple loci controlling a measurable trait. From these beginnings, computer simulation of evolution by biologists became more common in
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