Conference Proceeding

Adaptive evolutionary algorithms and extensions to the HyFlex hyper-heuristic framework

Citation

Ochoa G, Walker J, Hyde M & Curtois T (2012) Adaptive evolutionary algorithms and extensions to the HyFlex hyper-heuristic framework. In: Coello CC, Cutello V, Deb K K, Forrest S, Nicosia G & Pavone M (eds.) Parallel Problem Solving from Nature - PPSN XII: 12th International Conference, Taormina, Italy, September 1-5, 2012, Proceedings, Part II. Lecture Notes in Computer Science, Vol. 7492. 12th International Conference, Taormina, Italy, 01.09.2012-05.09.2012. Amsterdam: Springer, pp. 418-427. http://link.springer.com/chapter/10.1007%2F978-3-642-32964-7_42#

Abstract
HyFlex is a recently proposed software framework for implementing hyper-heuristics and domain-independent heuristic optimisation algorithms [13]. Although it was originally designed to implement hyper-heuristics, it provides a population and a set of move operators of different types. This enable the implementation of adaptive versions of other heuristics such as evolutionary algorithms and iterated local search. The contributions of this article are twofold. First, a number of extensions to the HyFlex framework are proposed and implemented that enable the design of more effective adaptive heuristics. Second, it is demonstrated that adaptive evolutionary algorithms can be implemented within the framework, and that the use of crossover and a diversity metric produced improved results, including a new best-known solution, on the studied vehicle routing problem.

StatusPublished
Title of seriesLecture Notes in Computer Science
Number in seriesVol. 7492
Publication date31/12/2012
Publication date online30/09/2012
URLhttp://hdl.handle.net/1893/15124
PublisherSpringer
Publisher URLhttp://link.springer.com/…-642-32964-7_42#
Place of publicationAmsterdam
ISSN of series0302-9743
ISBN978-3-642-32963-0
Conference12th International Conference
Conference locationTaormina, Italy
Dates

Research centres/groups