Abstract
Competitive co-evolutionary algorithms (CCEAs) have many advantages, but their range of applications has been crucially limited. This study provides a simple, non-problem-specific framework to extend that range. The framework has two co-evolving populations, one of candidate solutions and one of criteria, in which these populations competitively co-evolve with each other. The framework aims to avoid candidate solutions getting stuck in a local optimum by changing the fitness landscape dynamically. Moreover, the framework has a mechanism which will establish and maintain a proper arms race. We have conducted experiments on two function optimization problems, the 1-dimensional function maximization problem and the Rastrigin function minimization problem, in order to investigate the basic properties of the framework. The results of the experiments showed that a CCEA achieves a performance which is comparable to that of a GA.
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This work was presented in part at the 14th International Symposium on Artificial Life and Robotics, Oita, Japan, February 5–7, 2009
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Sato, T., Arita, T. Competitive co-evolutionary algorithms can solve function optimization problems. Artif Life Robotics 14, 440–443 (2009). https://doi.org/10.1007/s10015-009-0721-y
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DOI: https://doi.org/10.1007/s10015-009-0721-y