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A hierarchical solve-and-merge framework for multi-objective optimization

Mumford, Christine Lesley 2005. A hierarchical solve-and-merge framework for multi-objective optimization. Presented at: 2005 IEEE Congress on Evolutionary Computation, Edinburgh, UK, 2-5 September 2005. Published in: Mumford, Christine Lesley ed. The 2005 IEEE congress on evolutionary computation. IEEE, pp. 2241-2247. 10.1109/CEC.2005.1554973

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Abstract

This paper presents hierarchical solve-and-merge (HISAM): a two-stage approach to evolutionary multi-objective optimization. The first stage involves a simple genetic algorithm working on a number of isolated subpopulations, each using its own uniquely weighted linear scalarizing function to encourage it to focus on a different region of the Pareto space. At the second stage, the best solutions from stage one are passed to a Pareto-based hierarchy, where the solution set is judged on Pareto dominance and further improved. Preliminary results for large knapsack problems with 2-4 objectives are highly competitive with those obtained using other methods. Furthermore, the HISAM implementation has a fast execution time.

Item Type: Conference or Workshop Item (Paper)
Book Type: Edited Book
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Publisher: IEEE
ISBN: 0780393635
Related URLs:
Last Modified: 04 Jun 2017 04:03
URI: http://orca.cf.ac.uk/id/eprint/31896

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