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.

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.

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Researchers should cite this work as follows:

• Yongxin Yao (2011), "Genetic algorithm," https://nanohub.org/resources/geneticalgo. (DOI: 10.4231/D3X63B522).

  BibTex | EndNote

1. genetic algorithm
2. image
3. nanophotonics
4. Optimization