@inproceedings{4c89836a174f4af5a6a8fd7473f174e8,
title = "Modelling the population distribution in multi-objective optimization by generative topographic mapping",
abstract = "Under mild conditions, the Pareto set of a continuous multi-objective optimization problem exhibits certain regularity. We have recently advocated taking into consideration such regularity in designing multi-objective evolutionary algorithms. Following our previous work on using Local Principal Component Analysis for capturing the regularity, this paper presents a new approach for acquiring and using the regularity of the Pareto set in evolutionary algorithms. The approach is based on the Generative Topographic Mapping and can be regarded as an Estimation of Distribution Algorithm. It builds models of the distribution of promising solutions based on regularity patterns extracted from the previous search, and samples new solutions from the models thus built. The proposed algorithm has been compared with two other state-of-the-art algorithms, NSGA-II and SPEA2 on a set of test problems.",
author = "Aimin Zhou and Qingfu Zhang and Yaochu Jin and Bernhard Sendhoff and Edward Tsang",
year = "2006",
doi = "10.1007/11844297\_45",
language = "英语",
isbn = "3540389903",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "443--452",
booktitle = "Parallel Problem Solving from Nature, PPSN IX - 9th International Conference, Procedings",
address = "德国",
note = "9th International Conference on Parallel Problem Solving from Nature, PPSN 2006 ; Conference date: 09-09-2006 Through 13-09-2006",
}