Finding multiple first order saddle points using a valley adaptive clearing genetic algorithm
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Computers & Mathematics with Applications
Multi-modal valley-adaptive memetic algorithm for efficient discovery of first-order saddle points
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
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Recent studies [13, 18] have shown that clearing schemes are efficient multi-modal optimization methods. They efficiently reduce genetic drift which is the direct reason for premature convergence in genetic algorithms. However, clearing schemes assumed a landscape containing equal-spaced basins when using a fixed niche radius. Further, most clearing methods employ policies that favor elitists, thus affecting the explorative capabilities of the search. In this paper, we present a valley adaptive clearing scheme, aiming at adapting to non-uniform width of the valleys in the problem landscape. The framework of the algorithm involves hill-valley initialization, valley-adaptive clearing and archiving. Experimental results on benchmark functions are presented to demonstrate that the proposed scheme uncovers more local optima solutions and displays excellent robustness to varying niche radius than other clearing compeers.