A Painless Gradient-assisted Multi-objective Memetic Mechanism for Solving Continuous Bi-objective Optimization Problems

被引：0
作者：
Lara Lopez, Adriana [1 ]
Coello Coello, Carlos A. [1 ]
Schuetze, Oliver [1 ]
机构：
[1] CINVESTAV, IPN, Dept Computac, Mexico City 07360, DF, Mexico
来源：
2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2010年
关键词：
LOCAL SEARCH; ALGORITHMS; INFORMATION;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this work we present a simple way to introduce gradient-based information as a means to improve the search performed by a multi-objective evolutionary algorithm (MOEA). Our proposal can be easily incorporated into any MOEA, and is able to improve its performance when solving continuous bi-objective problems. We propose a novel mechanism to control the balance between the local search, and the global search performed by a MOEA. We discuss the advantages of the proposed method and its possible use when dealing with more objectives. Finally, we provide some guidelines regarding the use of our proposed approach.
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页数：8
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