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A Hyperheuristic Approach to Leveraging Domain Knowledge in Multi-Objective Evolutionary Algorithms

[+] Author Affiliations
Nozomi Hitomi, Daniel Selva

Cornell University, Ithaca, NY

Paper No. DETC2016-59870, pp. V02BT03A030; 10 pages
  • ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2B: 42nd Design Automation Conference
  • Charlotte, North Carolina, USA, August 21–24, 2016
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5011-4
  • Copyright © 2016 by ASME


Evolutionary algorithms have shown much success in solving real-world design problems, but they are considered computationally inefficient because they rely on many objective-function evaluations instead of leveraging domain knowledge to guide the optimization. An evolutionary algorithm’s performance can be improved by utilizing operators called domain-specific heuristics that incorporate domain knowledge, but existing knowledge-intensive algorithms utilize one or two domain-specific heuristics, which limits the amount of incorporated knowledge or treats all knowledge as equally effective. We propose a hyperheuristic approach that efficiently utilizes multiple domain-specific heuristics that incorporate knowledge from different sources by allocating computational resources to the effective ones. Furthermore, a hyperheuristic allows the simultaneous use of conventional evolutionary operators that assist in escaping local optima. This paper empirically demonstrates the efficacy of the proposed hyperheuristic approach on a multi-objective design problem for an Earth observation satellite system. Results show that the hyperheuristic approach significantly improves the search performance compared to an evolutionary algorithm that does not use any domain knowledge.

Copyright © 2016 by ASME



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