Genetic algorithms: Foundations and applications

Genetic algorithms are defined. Attention is directed to why they work: schemas and building blocks, implicit parallelism, and exponentially biased sampling of the better schema. Why they fail and how undesirable behavior can be overcome is discussed. Current genetic algorithm practice is summarized. Five successful applications are illustrated: image registration, AEGIS surveillance, network configuration, prisoner’s dilemma, and gas pipeline control. Three classes of problems for which genetic algorithms are ill suited are illustrated: ordering problems, smooth optimization problems, and ‘totally indecomposable’ problems.