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Evolutionary algorithm
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<p>Evolutionary algorithms (EA) reproduce essential elements of the <a href="/facts/Biological_evolution/TuWap5IK">biological evolution</a> in a <a href="/facts/Computer_algorithm/fnl5NmRt">computer algorithm</a> in order to solve "difficult" problems, at least <a href="/facts/Approximation/LvYHsXzq">approximately</a>, for which no exact or satisfactory solution methods are known. They belong to the class of <a href="/facts/Metaheuristic/ECF71Rez">metaheuristics</a> and are a <a href="/facts/Subset/SJGERmbA">subset</a> of <a href="/facts/Population_Based_Bio-Inspired_Algorithms/TLbLNF2U">population based bio-inspired algorithms</a> and <a href="/facts/Evolutionary_computation/qXyxPI8j">evolutionary computation</a>, which itself are part of the field of <a href="/facts/Computational_intelligence/GXpXlhu3">computational intelligence</a>. The mechanisms of biological evolution that an EA mainly imitates are <a href="/facts/Reproduction/nNu0F5Dy">reproduction</a>, <a href="/facts/Mutation/Ee4NnWbY">mutation</a>, <a href="/facts/Genetic_recombination/uocNH0m7">recombination</a> and <a href="/facts/Natural_selection/JXTte6en">selection</a>. <a href="/facts/Candidate_solution/cJUKNCmM">Candidate solutions</a> to the <a href="/facts/Optimization_problem/QzwJfFEE">optimization problem</a> play the role of individuals in a population, and the <a href="/facts/Fitness_function/Xeoc1I3C">fitness function</a> determines the quality of the solutions (see also <a href="/facts/Loss_function/xv5ozuhl">loss function</a>). <a href="/facts/Evolution/qE5amP6a">Evolution</a> of the population then takes place after the repeated application of the above operators. </p><p>Evolutionary algorithms often perform well approximating solutions to all types of problems because they ideally do not make any assumption about the underlying <a href="/facts/Fitness_landscape/XrqwdjWR">fitness landscape</a>. Techniques from evolutionary algorithms applied to the modeling of biological evolution are generally limited to explorations of <a href="/facts/Microevolution/Xwkz6zCf">microevolutionary processes</a> and planning models based upon cellular processes. In most real applications of EAs, computational complexity is a prohibiting factor. In fact, this computational complexity is due to fitness function evaluation. <a href="/facts/Fitness_approximation/PpSnne4V">Fitness approximation</a> 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. </p>