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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.

Keywords

genetic algorithms, flow shop scheduling, commonality hypothesis, search space reductions

Notes

Associated Center(s) / Consortia: Center for Integrated Manfacturing Decision Systems

Associated Lab(s) / Group(s): Intelligent Coordination and Logistics Laboratory

Text Reference

Stephen Chen and Stephen Smith , "Improving Genetic Algorithms by Search Space Reduction (with Applications to Flow Shop Scheduling)," GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference, 1999.

BibTeX Reference

@inproceedings{Chen_1999_556,
   author = "Stephen Chen and Stephen {Smith }",
   title = "Improving Genetic Algorithms by Search Space Reduction (with Applications to Flow Shop Scheduling)",
   booktitle = "GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference",
   publisher = "Morgan Kaufmann",
   year = "1999",
}

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