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The most established "classical" nature-inspired models of computation are '''cellular automata''', '''neural computation''', and '''evolutionary computation'''. More recent computational systems abstracted from natural processes include '''swarm intelligence''', '''artificial immune systems''',
'''artificial life''', '''membrane computing''', and '''amorphous computing'''.
In fact, all major methods and algorithms are nature-inspired
computation, swarm intelligence and others. The detailed review can be found in many books
<ref name="Olarius">Olarius S., Zomaya A. Y., Handbook of Bioinspired Algorithms and Applications, Chapman & Hall/CRC, 2005.</ref>
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An artificial evolutionary system is a computational system based on the notion of simulated evolution. It comprises a constant- or variable-size population of individuals, a [[fitness (biology)|fitness criterion]], and genetically inspired operators that produce the next '''[[generation]]''' from the current one.
The initial population is typically generated randomly or heuristically, and typical operators
are '''[[mutation]]''' and '''[[genetic recombination|recombination]]'''. At each step, the individuals are evaluated according to the given fitness function ('''[[survival of the fittest]]'''). The next generation is obtained from selected individuals (parents) by using genetically inspired operators. The choice of parents can be guided by a selection operator which reflects the biological principle of [[mate selection]]. This process of simulated [[evolution]] eventually converges towards a nearly optimal population of individuals, from the point of view of the fitness function.
The study of evolutionary systems has historically evolved along three main branches:
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