The Cooperative Royal Road: Avoiding Hitchhiking

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4926)


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.


Genetic Algorithm Local Search Method String Length Evolutionary Game Theory Standard Genetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  1. 1.Automated Scheduling, Optimisation and Planning Group, School of Computer Science & ITUniversity of NottinghamNottinghamUK
  2. 2.COMPLEX Team, INRIA RocquencourtDomaine de VoluceauLe Chesnay CedexFrance

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