Abstract
This paper investigates the exploitation of non-dominated 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.
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Kort, S. (2003). Schemata-Driven Multi-objective Optimization. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_14
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DOI: https://doi.org/10.1007/3-540-36970-8_14
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