Recent Developments in the Evolution Strategies of Genetic Algorithms: Theory and Applications

This paper provides a review on current developments in genetic algorithms. The discussion includes theoretical aspects of genetic algorithms and genetic algorithm applications. Theoretical topics under review include genetic algorithm techniques, genetic operator techniques, niching techniques, genetic drift,, method of benchmarking genetic algorithm performances, measurement of difficulty level of a test-bed function,population genetics and developmental mechanism in genetic algorithms. Examples of genetic algorithm application in this review are pattern recognition, robotics, artificial life, expert system, electronic circuit design, cellular automata and biological applications.While the paper covers many works on the theory and application of genetic algorithms, not much details are reported on genetic programming, parallel genetic algorithms, in addition to more advanced techniques e.g. micro genetic algorithms and multiobjective optimisation.