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=== Evolutionary computation ===
[[Evolutionary computation]] is a field in soft computing that uses the principles of [[natural selection]] and [[evolution]] to solve complicated problems. It promotes the discovery of diverse solutions within a solution space, encouraging near-perfect solutions. It finds satisfactory solutions by using computational models and types of [[Evolutionary algorithm|evolutionary algorithms]]. Evolutionary computation consists of algorithms that mimic natural selection, such as [[genetic algorithm]]s, [[genetic programming]], [[Evolution strategy|evolution strategies]] and [[evolutionary programming]]. These algorithms use [[Crossover (genetic algorithm)|crossover]], [[Mutation (genetic algorithm)|mutation]], and [[Selection (genetic algorithm)|selection]].<ref>{{Cite web |date=2017-06-29 |title=Genetic Algorithms |url=https://www.geeksforgeeks.org/genetic-algorithms/ |access-date=2023-11-11 |website=GeeksforGeeks |language=en-US}}</ref>
Crossover, or recombination, exchanges data between nodes to diversify data and handle more outcomes. [[Mutation]] is a genetic technique that helps prevent the premature conclusion to a suboptimal solution by diversifying an entire range of solutions. It helps new optimal solutions in solution sets that help the overall optimization process. Selection is an operator that chooses which solution from a current population fits enough to transition to the next phase. These drive genetic programming to find optimal solutions by ensuring the survival of only the fittest solutions in a set.
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