Schemata-driven multi-objective optimization

Authors:
Skander Kort
Affiliations:
Concordia University, ECE Department, Montreal, PQ, Canada
Venue:
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Year:
2003

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Abstract

This paper investigates the exploitation of nondominated sets' schemata in guiding multi-objective optimization. Schemata capture the similarities between solutions in the non-dominated set. They also reflect the knowledge acquired by multi-objective evolutionary algorithms. A schemata-driven genetic algorithm as well as a schemata-driven local search algorithm are described. An experimental study to evaluate the suggested approach is then conducted.