@inproceedings{2d932806e86e47358ade361d4c4564e4,
title = "A self-adaptive evolutionary algorithm for multi-objective optimization",
abstract = "Evolutionary algorithm has gained a worldwide popularity among multi-objective optimization. The paper proposes a self-adaptive evolutionary algorithm (called SEA) for multi-objective optimization. In the SEA, the probability of crossover and mutation, Pc and Pm, are varied depending on the fitness values of the solutions. Fitness assignment of SEA realizes the twin goals of maintaining diversity in the population and guiding the population to the true Pareto Front; fitness value of individual not only depends on improved density estimation but also depends on non-dominated rank. The density estimation can keep diversity in all instances including when scalars of all objectives are much different from each other. SEA is compared against the Non-dominated Sorting Genetic Algorithm (NSGA-II) on a set of test problems introduced by the MOEA community. Simulated results show that SEA is as effective as NSGA-II in most of test functions, but when scalar of objectives are much different from each other, SEA has better distribution of non-dominated solutions.",
keywords = "Evolutionary algorithm, Multi-objective optimization, Nondominated, SEA",
author = "Ruifen Cao and Guoli Li and Yican Wu",
year = "2007",
doi = "10.1007/978-3-540-74205-0\_60",
language = "英语",
isbn = "9783540742012",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "553--564",
booktitle = "Advanced Intelligent Computing Theories and Applications",
note = "3rd International Conference on Intelligent Computing, ICIC 2007 ; Conference date: 21-08-2007 Through 24-08-2007",
}