@inproceedings{2b03d38122b24cf6acc846e2980f85b1,
title = "A model-based evolutionary algorithm for Bi-objective optimization",
abstract = "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.",
author = "Aimin Zhou and Qingfu Zhang and Yaochu Jin and Edward Tsang and Tatsuya Okabe",
year = "2005",
language = "英语",
isbn = "0780393635",
series = "2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings",
pages = "2568--2575",
booktitle = "2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings",
note = "2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 ; Conference date: 02-09-2005 Through 05-09-2005",
}