Genetic algorithms: Difference between revisions

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*Bies, Robert R; Muldoon, Matthew F; Pollock, Bruce G; Manuck, Steven; Smith, Gwenn and Sale, Mark E(2006), ''A Genetic Algorithm-Based, Hybrid Machine Learning Approach to Model Selection'' Journal of Pharmacokinetics and Pharmacodynamics Springer-Netherlands pp. 196-221
*Crosby, Jack L. (1973), ''Computer Simulation in Genetics,''  John Wiley & Sons, London.
*Falkenauer, Emanuel (1997), ''Genetic Algorithms and Grouping Problems,'' John Wiley & Sons Ltd, Chichester, England. ISBN 978-0-471-97150-4
*Fentress, Sam W (2005), ''Exaptation as a means of evolving complex solutions,'' MA Thesis, University of Edinburgh. ([http://www.inf.ed.ac.uk/publications/thesis/online/IM050329.pdf pdf])
*Fogel, David B. (2000) ''Evolutionary Computation: Towards a New Philosophy of Machine Intelligence'' IEEE Press, New York.
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*Goldberg, David E (1989), ''Genetic Algorithms in Search, Optimization and Machine Learning,'' Kluwer Academic Publishers, Boston, MA.
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In computer science and operations research, 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.

A genetic algorithm is a meta-heuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms[1]. Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of their applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, and causal inference.

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Footnotes

  1. Wikipedia has details about evolutionary algorithms.