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An experimental study of combining evolutionary algorithms with KD-tree to solving dynamic optimisation problems

Nguyen, TT and Jenkinson, I and Yang, Z (2015) An experimental study of combining evolutionary algorithms with KD-tree to solving dynamic optimisation problems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9028. pp. 857-868. ISSN 0302-9743

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Abstract

This paper studies the idea of separating the explored and unexplored regions in the search space to improve change detection and optima tracking. When an optimum is found, a simple sampling technique is used to estimate the basin of attraction of that optimum. This estimated basin is marked as an area already explored. Using a special tree-based data structure named KD-Tree to divide the search space, all explored areas can be separated from unexplored areas. Given such a division, the algorithm can focus more on searching for unexplored areas, spending only minimal resource on monitoring explored areas to detect changes in explored regions. The experiments show that the proposed algorithm has competitive performance, especially when change detection is taken into account in the optimisation process. The new algorithm was proved to have less computational complexity in term of identifying the appropriate sub-population/region for each individual. We also carry out investigations to find out why the algorithm performs well. These investigations reveal a positive impact of using the KD-Tree.

Item Type: Article
Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-16549-3_69
Uncontrolled Keywords: 08 Information And Computing Sciences
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Maritime and Mechanical Engineering
Publisher: Springer Verlag
Date Deposited: 30 Apr 2015 13:56
Last Modified: 09 Nov 2016 12:30
DOI or Identification number: 10.1007/978-3-319-16549-3_69
URI: http://researchonline.ljmu.ac.uk/id/eprint/961

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