A model-based evolutionary algorithm for Bi-objective optimization
Zhou, A, Zhang, Q, Jin, Y, Tsang, E and Okabe, T (2005) A model-based evolutionary algorithm for Bi-objective optimization In: IEEE Congress on Evolutionary Computation, CEC 2005, 2005-09-02 - 2005-09-05, Edinburgh, UK.
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The Pareto optimal solutions to a multi-objective optimization problem often distribute very regularly in both the decision space and the objective space. Most existing evolutionary algorithms do not explicitly take advantage of such a regularity. This paper proposed a model-based evolutionary algorithm (M-MOEA) for bi-objective optimization problems. Inspired by the ideas from estimation of distribution algorithms, M-MOEA uses a probability model to capture the regularity of the distribution of the Pareto optimal solutions. The local principal component analysis (local PCA) and the least-squares method are employed for building the model. New solutions are sampled from the model thus built. At alternate generations, M-MOEA uses crossover and mutation to produce new solutions. The selection in M-MOEA is the same as in non-dominated sorting genetic algorithm-II (NSGA-II). Therefore, MOEA can be regarded as a combination of EDA and NSGA-II. The preliminary experimental results show that M-MOEA performs better than NSGA-II.
|Item Type:||Conference or Workshop Item (Paper)|
|Additional Information:||© 2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works|
|Divisions:||Faculty of Engineering and Physical Sciences > Computing Science|
|Depositing User:||Symplectic Elements|
|Date Deposited:||08 Mar 2012 16:29|
|Last Modified:||23 Sep 2013 18:49|
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