Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
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发表时间：
1993-06
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通讯作者：
C. Fonseca;P. Fleming
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作者：
C. Fonseca;P. Fleming
本文描述了一种用于多目标遗传算法（MOGAs）的基于排序的适应度分配方法。将传统的小生境形成方法扩展到这类多模态问题，并提出了确定小生境大小的理论。然后对适应度分配方法进行修改，以允许外部决策者（DM）直接干预。最后，对多目标遗传算法进行进一步推广：遗传算法被视为多目标优化循环的优化元素，该循环还包括决策者。正是两者之间的相互作用导致确定问题的满意解。给出了决策者如何与遗传算法相互作用的说明性结果。它们还展示了多目标遗传算法对权衡面区域进行均匀采样的能力。
The paper describes a rank-based (cid:12)tness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formationmethods are extended to this class of multimodal problems and theory for setting the niche size is presented. The (cid:12)t-ness assignment method is then modi(cid:12)ed to allow direct intervention of an external decision maker (DM). Finally, the MOGA is generalised further: the genetic algorithmis seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM. It is the interaction between the two that leads to the determination of a satisfactory solution to the problem. Illustrative results of how the DM can interact with the genetic algorithm are presented. They also show the ability of the MOGA to uniformly sample regions of the trade-o(cid:11) surface.