Genetic Algorithms: Theory And Application

Genetic algorithms, a class of robust and efficient search techniques that can be randomly sample large spaces, have applications in the field of optimization and in a wide range of computer science problems in pattern recognition, search, scheduling, and machine learning. Genetic algorithms are motivated by characteristics found in natural population genetics, among them robustness and efficiency. Features of biological systems found in genetic algorithms include reproduction, selfguidance, self-repair, the nature of survival of the fittest, and variation through mutation. Genetic algorithms were developed by John Holland of the University of Michigan in the 1970's. Many of the essential properties of genetic algorithms discussed in this paper can be found in [1, 2].