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. I...

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Bibliographic Details
Published in2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM) pp. 72 - 77
Main Authors ZhiChao Shi, Rui Wang, Tao Zhang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2014
Subjects
Benchmark testing
Evolutionary computation
fitness assignment
multi-objective optimization
Pareto optimization
Sociology
Vectors
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Summary: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.
DOI:10.1109/MCDM.2014.7007190