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Genetic algorithm

By Yongxin Yao

Iowa State University

A simple cut-paste-mutation genetic evolution method

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Version 1.2 - published on 14 Aug 2014

doi:10.4231/D3V97ZS30 cite this

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Abstract

We present a simple genetic algorithm based on cut-paste-mutation operations to solve problems with complexity scales as 2^N, where N is the size of the system. Entertaining examples of evolving to nice 2D pictures are chosen for illustration, where the complexity increases exponentially with the number of pixels. Interesting academic questions, like how the population size, number of parents for each generation, mutation rate, and probability the fitness criteria will affect the efficiency of evolution,can be studied.

Sponsored by

Work at the Ames laboratory was supported by the U.S. Department of Energy, Office of Basic Energy Science, in- cluding a grant of computer time at the National Energy Research Supercomputing Center (NERSC) at the Lawrence Berkeley National Laboratory under Contract No. DE-AC02- 07CH11358.

References

1.Deaven, D.M. & Ho, K.M. Molecular Geometry Optimization with a Genetic Algorithm. Phys. Rev. Lett. 75, 288 (1995). 2.Yao, Y.X., Chan, T.L., Wang, C.Z., & Ho, K.M. "Structures and energetics of hydrocarbon molecules in the full hydrogen chemical potential range". Oral presentation at the 2007 APS March meeting, Denver, Colorado, March 5–9, 2007.

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  • Yongxin Yao (2014), "Genetic algorithm," http://nanohub.org/resources/geneticalgo. (DOI: 10.4231/D3V97ZS30).

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