Book Chapter

The cooperative royal road: Avoiding Hitchhiking

Details

Citation

Ochoa G, Lutton E & Burke E (2008) The cooperative royal road: Avoiding Hitchhiking. In: Monmarche N, Talbi E, Collet P, Schoenauer M & Lutton E (eds.) Artificial Evolution: 8th International Conference, Evolution Artificielle, EA 2007, Tours, France, October 29-31, 2007, Revised Selected Papers. Lecture Notes in Computer Science, 4926. Berlin Heidelberg: Springer, pp. 184-195. http://link.springer.com/chapter/10.1007%2F978-3-540-79305-2_16?LI=true#; https://doi.org/10.1007/978-3-540-79305-2_16

Abstract
We propose using the so called Royal Road functions as test functions for cooperative co-evolutionary algorithms (CCEAs). The Royal Road functions were created in the early 90's with the aim of demonstrating the superiority of genetic algorithms over local search methods. Unexpectedly, the opposite was found to be true. The research deepened our understanding of the phenomenon of hitchhiking where unfavorable alleles may become established in the population following an early association with an instance of a highly fit schema. Here, we take advantage of the modular and hierarchical structure of the Royal Road functions to adapt them to a co-evolutionary setting. Using a multiple population approach, we show that a CCEA easily outperforms a standard genetic algorithm on the Royal Road functions, by naturally overcoming the hitchhiking effect. Moreover, we found that the optimal number of sub-populations for the CCEA is not the same as the number of components that the function can be linearly separated into, and propose an explanation for this behavior. We argue that this class of functions may serve in foundational studies of cooperative co-evolution.

StatusPublished
Title of seriesLecture Notes in Computer Science
Number in series4926
Publication date31/12/2008
URLhttp://hdl.handle.net/1893/11075
PublisherSpringer
Publisher URLhttp://link.springer.com/…05-2_16?LI=true#
Place of publicationBerlin Heidelberg
ISSN of series0302-9743
ISBN978-3-540-79304-5

People (1)

People

Professor Gabriela Ochoa

Professor Gabriela Ochoa

Professor, Computing Science