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dbr:Evolutionary_computation
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Evolutionary computation
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In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.
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dbr:Human-based_evolutionary_computation dbr:No_free_lunch_in_search_and_optimization dbr:Algorithm dbr:Historical_inheritance_systems dbr:Kenneth_A_De_Jong dbr:Ant_colony_optimization_algorithms dbr:EvoStar dbr:David_E._Goldberg dbr:Parallel_Problem_Solving_from_Nature dbr:Riccardo_Poli dbr:Genetic_programming dbr:Hans-Paul_Schwefel dbr:Genetic_recombination dbr:Peter_J._Fleming dbr:Interactive_evolutionary_computation dbr:Cultural_algorithm dbr:Self-organizing_map dbr:MIT_Press dbr:Evolution_strategy dbr:Artificial_intelligence dbr:Stochastic_optimization dbr:Genetic_algorithm dbr:John_Koza dbr:Agent-based_model dbr:Mutation dbr:Program_synthesis dbr:Selective_breeding dbr:Springer_Publishing dbr:Evolved_antenna dbr:University_of_Michigan_Press dbr:Adaptive_dimensional_search dbr:Stephanie_Forrest dbr:Learning_classifier_system dbr:Zbigniew_Michalewicz dbr:Inferential_programming dbr:Mutation_testing dbr:Genetic_and_Evolutionary_Computation_Conference dbc:Evolution dbr:Autoconstructive_evolution dbr:Particle_swarm_optimization dbr:Reproduction dbr:Swarm_intelligence dbr:Peter_Nordin dbr:Feasible_region dbr:Grammatical_evolution dbr:Nils_Aall_Barricelli dbr:Association_for_Computing_Machinery dbr:Competitive_learning dbr:David_B._Fogel dbr:Learnable_evolution_model dbr:Fitness_(biology) dbr:Evolutionary_programming dbr:Dynamical_system dbr:John_Henry_Holland dbr:Theo_Jansen dbr:Estimation_of_distribution_algorithm dbr:Stochastic dbr:Digital_organism dbc:Evolutionary_computation dbr:Ingo_Rechenberg dbr:Computer_science dbr:Survival_of_the_fittest dbr:Memetic_algorithm dbr:Fitness_approximation dbr:Melanie_Mitchell dbr:Developmental_biology dbr:Evolution dbr:Artificial_development dbr:Metaheuristic dbr:IEEE_Congress_on_Evolutionary_Computation dbr:Evolutionary_robotics dbr:Differential_evolution dbr:Population dbr:Neuroevolution dbr:Gene_expression_programming dbr:Test_functions_for_optimization dbr:Universal_Darwinism dbr:Kalyanmoy_Deb dbr:Fitness_function dbr:Fitness_landscape dbr:Evolutionary_algorithm dbr:Global_optimization dbr:Artificial_life dbr:Mathematical_optimization dbr:Natural_selection dbr:Self-organization dbr:Systems_biology dbr:Theory_of_computation dbr:Biological_system dbr:Alex_Fraser_(scientist) dbr:Lawrence_J._Fogel dbr:Evolutionary_biology dbr:Completeness_(logic) dbr:Soft_computing dbr:Lecture_Notes_in_Computer_Science dbr:Trial_and_error dbr:Loss_function dbr:Dual-phase_evolution dbr:Artificial_immune_system dbr:Genetic_operator
dbo:abstract
In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character. In evolutionary computation, an initial set of candidate solutions is generated and iteratively updated. Each new generation is produced by stochastically removing less desired solutions, and introducing small random changes. In biological terminology, a population of solutions is subjected to natural selection (or artificial selection) and mutation. As a result, the population will gradually evolve to increase in fitness, in this case the chosen fitness function of the algorithm. Evolutionary computation techniques can produce highly optimized solutions in a wide range of problem settings, making them popular in computer science. Many variants and extensions exist, suited to more specific families of problems and data structures. Evolutionary computation is also sometimes used in evolutionary biology as an in silico experimental procedure to study common aspects of general evolutionary processes.
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