Abstract
In solving highly dimensional multi-objective optimization (EMO) problems by evolutionary computations the concept of Pareto-domination appears to be not effective. The paper discusses a new approach to EMO by introducing a concept of genetic genders for the purpose of making distinction between different groups of objectives. This approach is also able to keep diversity among the Pareto-optimal solutions produced.
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Kowalczuk, Z., Bialaszewski, T. (2006). Improving Evolutionary Multi-objective Optimization Using Genders. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_42
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DOI: https://doi.org/10.1007/11785231_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35748-3
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