Genetic algorithms

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Genetic algorithms or GAs view learning as a competition among a population of evolving candidate problem solutions. A 'fitness' function estimates each solution for deciding whether it will contribute to the next generation of solutions or not. After that, as in gene transfer in sexual reproduction, the algorithm creates a new population of candidate solutions.

References

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  • Schmitt, Lothar M (2004), Theory of Genetic Algorithms II: models for genetic operators over the string-tensor representation of populations and convergence to global optima for arbitrary fitness function under scaling, Theoretical Computer Science (310), pp. 181-231
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