Abstract
The Multi-objective Genetic Algorithms (MOGA) based on Pareto Optimum have been widely applied to solve multi-objective optimal problems, mainly because of their ability to find a set of candidate solutions within a single run. In MOGAs, a non-dominated set is a set of candidate solutions, so it is very important to construct the non-dominated set efficiently. In this paper, the relation of individuals and their related features are discussed. It is proved that the individuals of an evolutionary population can be sorted by quick sort. We construct the non-dominated set of the evolutionary population with quick sort, and the time complexity of the construction is O(nlog n), compared to the previous best result of O(n 2) described in the popular NSGA-II [Deb, 2002]. We further propose a multi-objective genetic algorithm based on quick sort, and two benchmark problems are experimented. We show that the results of the experiments match to our theoretical analysis.
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Zheng, J., Ling, C., Shi, Z., Xue, J., Li, X. (2004). A Multi-objective Genetic Algorithm Based on Quick Sort. In: Tawfik, A.Y., Goodwin, S.D. (eds) Advances in Artificial Intelligence. Canadian AI 2004. Lecture Notes in Computer Science(), vol 3060. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24840-8_13
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DOI: https://doi.org/10.1007/978-3-540-24840-8_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22004-6
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