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Genetic Algorithms (GAs) are adaptive methods which may be used to solve search and optimisation problems. They are based on the genetic processes of biological organisms. Over many generations, natural populations evolve according to the principles of natural selection and\survival of the ttest", rst clearly stated by Charles Darwin in The Origin of Species. By mimicking this process, genetic algorithms are able to\evolve" solutions to real world problems, if they have been suitably encoded. For example, GAs can be used to design bridge structures, for maximum strength/weight ratio, or to determine the least wasteful layout for cutting shapes from cloth. They can also be used for online process control, such as in a chemical plant, or load balancing on a multi-processor computer system.

The basic principles of GAs were rst laid down rigourously by Holland Hol75], and are well described in many texts (eg Dav87, Dav91, Gre86, Gre90, Gol89a, Mic92]). GAs simulate those processes in natural populations which are essential to evolution. Exactly which biological processes are essential for evolution, and which processes have little or no role to play is still a matter for research but the foundations are clear. In nature, individuals in a population compete with each other for resources such as food, water and shelter. Also, members of the same species often compete to attract a mate. Those individuals which are most successful in surviving and attracting mates will have relatively larger numbers of o spring. Poorly performing individuals will produce few of even no o spring at all. This means that the genes from the highly adapted, or\t" individuals will …