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Evolutionary biology
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Darwin's finches by John Gould
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Processes and outcomes Population genetics Variation Diversity Mutation Natural selection Adaptation Polymorphism Genetic drift Gene flow Speciation Adaptive radiation Co-operation Coevolution Coextinction Divergence Convergence Parallel evolution Extinction
Natural history Origin of life Common descent History of life Timeline of evolution Human evolution Phylogeny Biodiversity Biogeography Classification Evolutionary taxonomy Cladistics Transitional fossil Extinction event
History of evolutionary theory Overview Renaissance Before Darwin Darwin Origin of Species Before synthesis Modern synthesis Molecular evolution Evo-devo Current research History of speciation History of paleontology (timeline)
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Evolutionary algorithm
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Artificial development Artificial life Cellular evolutionary algorithm Cultural algorithm Differential evolution Effective fitness Evolutionary computation Evolution strategy Gaussian adaptation Grammar induction Evolutionary multimodal optimization Particle swarm optimization Memetic algorithm Natural evolution strategy Neuroevolution Promoter based genetic algorithm Spiral optimization algorithm Self-modifying code Polymorphic code
Genetic algorithm
Chromosome Clonal selection algorithm Crossover Mutation Genetic memory Genetic fuzzy systems Selection Fly algorithm
Genetic programming
Cartesian genetic programming Linear genetic programming Grammatical evolution Multi expression programming Genetic Improvement Schema Eurisko Parity benchmark
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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.

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 as well as, depending on the method, mixing parental information. In biological terminology, a population of solutions is subjected to natural selection (or artificial selection), mutation and possibly recombination. 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.