PICEA-g采用增强型适应度分配方法

2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM) Pub Date : 2014-12-01 DOI:10.1109/MCDM.2014.7007190

Zhichao Shi, Rui Wang, Zhang Tao

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引用次数: 8

摘要

基于目标向量的偏好启发协同进化算法(PICEA-g)已被证明在多目标问题上表现良好。PICEA-g算法的优势在于智能适应度分配，即候选解与目标向量在搜索过程中协同进化。在本研究中，我们发现了这种适应度分配方法的局限性，并提出了一种增强的适应度分配方法，该方法同时考虑了目标向量的性能和Pareto优势秩对候选解适应度计算的影响。实验结果表明，采用改进方法的PICEA-g是有效的，特别是对于双目标问题。

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PICEA-g using an enhanced fitness assignment method

The preference-inspired co-evolutionary algorithm using goal vectors (PICEA-g) has been demonstrated to perform well on multi-objective problems. The superiority of PICEA-g originates from the smart fitness assignment, that is, candidate solutions are co-evolved with goal vectors along the search. In this study, we identify a limitation of this fitness assignment method, and propose an enhanced fitness assignment method which considers both the performance of goal vectors and the Pareto dominance rank on the fitness calculation of candidate solutions. Experimental results show that PICEA-g with the enhanced approach is effective, especially for bi-objective problems.

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2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)

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