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Memetic algorithm
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<p>In <a href="/facts/Computer_science/lN3pEyPz">computer science</a> and <a href="/facts/Operations_research/H5yJkV5n">operations research</a>, a memetic algorithm (MA) is an extension of an <a href="/facts/Evolutionary_algorithm/bo7eHlcA">evolutionary algorithm</a> (EA) that aims to accelerate the evolutionary search for the <a href="/facts/Optimum/oRn8Iv5I">optimum</a>. An EA is a <a href="/facts/Metaheuristic/ECF71Rez">metaheuristic</a> that reproduces the basic principles of <a href="/facts/Biological_evolution/TuWap5IK">biological evolution</a> as a <a href="/facts/Computer_algorithm/fnl5NmRt">computer algorithm</a> in order to solve challenging <a href="/facts/Optimization_problem/QzwJfFEE">optimization</a> or <a href="/facts/Planning/ckzCp5tv">planning</a> tasks, at least <a href="/facts/Approximation/LvYHsXzq">approximately</a>. An MA uses one or more suitable <a href="/facts/Heuristic/BcBEkv9c">heuristics</a> or <a href="/facts/Local_search_(optimization)/G1D2aHBz">local search</a> techniques to improve the quality of solutions generated by the EA and to speed up the search. The effects on the <a href="/facts/Premature_convergence/1qDpBNRS">reliability of finding the global optimum</a> depend on both the use case and the design of the MA. </p><p>Memetic algorithms represent one of the recent growing areas of research in <a href="/facts/Evolutionary_computation/qXyxPI8j">evolutionary computation</a>. The term MA is now widely used as a synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. Quite often, MAs are also referred to in the literature as Baldwinian evolutionary algorithms, Lamarckian EAs, cultural algorithms, or genetic local search. </p>