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- Evolutionary_algorithm abstract "In artificial intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions (see also loss function). Evolution of the population then takes place after the repeated application of the above operators. Artificial evolution (AE) describes a process involving individual evolutionary algorithms; EAs are individual components that participate in an AE.Evolutionary algorithms often perform well approximating solutions to all types of problems because they ideally do not make any assumption about the underlying fitness landscape; this generality is shown by successes in fields as diverse as engineering, art, biology, economics, marketing, genetics, operations research, robotics, social sciences, physics, politics and chemistry[citation needed].Techniques from evolutionary algorithms applied to the modeling of biological evolution are generally limited to explorations of microevolutionary processes. The computer simulations Tierra and Avida attempt to model macroevolutionary dynamics.In most real applications of EAs, computational complexity is a prohibiting factor. In fact, this computational complexity is due to fitness function evaluation. Fitness approximation is one of the solutions to overcome this difficulty. However, seemingly simple EA can solve often complex problems; therefore, there may be no direct link between algorithm complexity and problem complexity.A possible limitation of many evolutionary algorithms is their lack of a clear genotype-phenotype distinction. In nature, the fertilized egg cell undergoes a complex process known as embryogenesis to become a mature phenotype. This indirect encoding is believed to make the genetic search more robust (i.e. reduce the probability of fatal mutations), and also may improve the evolvability of the organism. Such indirect (aka generative or developmental) encodings also enable evolution to exploit the regularity in the environment. Recent work in the field of artificial embryogeny, or artificial developmental systems, seeks to address these concerns. And gene expression programming successfully explores a genotype-phenotype system, where the genotype consists of linear multigenic chromosomes of fixed length and the phenotype consists of multiple expression trees or computer programs of different sizes and shapes.".
- Evolutionary_algorithm wikiPageExternalLink EASY.
- Evolutionary_algorithm wikiPageExternalLink EvolutionaryOptimization.
- Evolutionary_algorithm wikiPageExternalLink gp-field-guide.
- Evolutionary_algorithm wikiPageExternalLink ec.
- Evolutionary_algorithm wikiPageExternalLink ga_demo.
- Evolutionary_algorithm wikiPageExternalLink www.midaco-solver.com.
- Evolutionary_algorithm wikiPageExternalLink genalg.html.
- Evolutionary_algorithm wikiPageID "190837".
- Evolutionary_algorithm wikiPageRevisionID "602085577".
- Evolutionary_algorithm hasPhotoCollection Evolutionary_algorithm.
- Evolutionary_algorithm subject Category:Cybernetics.
- Evolutionary_algorithm subject Category:Evolution.
- Evolutionary_algorithm subject Category:Evolutionary_algorithms.
- Evolutionary_algorithm subject Category:Optimization_algorithms_and_methods.
- Evolutionary_algorithm type Abstraction100002137.
- Evolutionary_algorithm type Act100030358.
- Evolutionary_algorithm type Activity100407535.
- Evolutionary_algorithm type Algorithm105847438.
- Evolutionary_algorithm type Event100029378.
- Evolutionary_algorithm type EvolutionaryAlgorithms.
- Evolutionary_algorithm type OptimizationAlgorithmsAndMethods.
- Evolutionary_algorithm type Procedure101023820.
- Evolutionary_algorithm type PsychologicalFeature100023100.
- Evolutionary_algorithm type Rule105846932.
- Evolutionary_algorithm type YagoPermanentlyLocatedEntity.
- Evolutionary_algorithm comment "In artificial intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions (see also loss function).".
- Evolutionary_algorithm label "Algorithme évolutionniste".
- Evolutionary_algorithm label "Algoritmo evolutivo".
- Evolutionary_algorithm label "Algoritmo evolutivo".
- Evolutionary_algorithm label "Algoritmo evolutivo".
- Evolutionary_algorithm label "Algorytm ewolucyjny".
- Evolutionary_algorithm label "Evolutionary algorithm".
- Evolutionary_algorithm label "Evolutionärer Algorithmus".
- Evolutionary_algorithm label "Эволюционные алгоритмы".
- Evolutionary_algorithm label "進化的アルゴリズム".
- Evolutionary_algorithm sameAs Evolutionärer_Algorithmus.
- Evolutionary_algorithm sameAs Algoritmo_evolutivo.
- Evolutionary_algorithm sameAs Algorithme_évolutionniste.
- Evolutionary_algorithm sameAs Algoritmo_evolutivo.
- Evolutionary_algorithm sameAs 進化的アルゴリズム.
- Evolutionary_algorithm sameAs Algorytm_ewolucyjny.
- Evolutionary_algorithm sameAs Algoritmo_evolutivo.
- Evolutionary_algorithm sameAs m.01b23t.
- Evolutionary_algorithm sameAs Q14489129.
- Evolutionary_algorithm sameAs Q14489129.
- Evolutionary_algorithm sameAs Evolutionary_algorithm.
- Evolutionary_algorithm wasDerivedFrom Evolutionary_algorithm?oldid=602085577.
- Evolutionary_algorithm isPrimaryTopicOf Evolutionary_algorithm.