Improving Genetic Algorithms by Search Space Reduction (with Applications to Flow Shop Scheduling)

Abstract

Crossover operators that preserve common components can also preserve representation level constraints. Consequently, these constraints can be used to beneficially reduce the search space. For example, in flow shop scheduling problems with order-based objectives (e.g. tardiness costs and earliness costs), search space reductions have been implemented with prece dence constraints. Experiments show that these (heuristically added) constraints can significantly improve the performance of Precedence Preserving Crossover--an operator which preserves common (order-based) schemata. Conversely, the performance of Uniform Order- Based Crossover (the best traditional sequencing operator) improves less--it is based on combination. Overall, the results suggest that condi tions exist where Precedence Preserving Crossover should be the best performing genetic sequencing operator.