Improving Evolutionary Multi-objective Optimization Using Genders Artificial Intelligence and Soft Computing – ICAISC 2006
edited by: Leszek Rutkowski, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Żurada
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.