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[edit]

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* Schmitt, Lothar M, Nehaniv Chrystopher N, Fujii Robert H (1998), Linear analysis of genetic algorithms, Theoretical Computer Science (208), pp. 111-148
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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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* Wright, A.H. et al. (2003) Implicit Parallelism in Proceedings of the Genetic and Evolutionary Computation Conference 2003

## External links[edit]

* A Genetic Algorithm Tutorial by Darrell Whitley Computer Science Department Colorado State University An excellent tutorial with lots of theory
* Another tutorial
* Another tutorial