|Appears in Collections:||Computing Science and Mathematics Book Chapters and Sections|
|Title:||The cooperative royal road: Avoiding Hitchhiking|
|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|
|Series/Report no.:||Lecture Notes in Computer Science, 4926|
|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.|
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