A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Global optimization
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
An Analysis of Evolutionary Algorithms Based on Neighborhood and Step Sizes
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
An Experimental Investigation of Self-Adaptation in Evolutionary Programming
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation
Combining mutation operators in evolutionary programming
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Population size reduction for the differential evolution algorithm
Applied Intelligence
A mixed strategy of combining evolutionary algorithms with multigrid methods
International Journal of Computer Mathematics
Evolutionary programming with ensemble of explicit memories for dynamic optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Ensemble strategies with adaptive evolutionary programming
Information Sciences: an International Journal
Information Sciences: an International Journal
Ensemble of constraint handling techniques
IEEE Transactions on Evolutionary Computation
A game-theoretic approach for designing mixed mutation strategies
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
A modification to classical evolutionary programming by shifting strategy parameters
Applied Intelligence
Adaptive learning algorithm of self-organizing teams
Expert Systems with Applications: An International Journal
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The lognormal self-adaptation has been used extensively in evolutionary programming (EP) and evolution strategies (ES) to adjust the search step size for each objective variable. However, it was discovered in our previous study (K.-H. Liang, X. Yao, Y. Liu, C. Newton, and D. Hoffman, in iEvolutionary Programming VII. Proc. of the Seventh Annual Conference on Evolutionary Programming, vol. 1447, edited by V. Porto, N. Saravanan, D. Waagen, and A. Eiben, Lecture Notes in Computer Science, Springer: Berlin, pp. 291–300, 1998) that such self-adaptation may rapidly lead to a search step size that is far too small to explore the search space any further, and thus stagnates search. This is called ithe loss of step size control. It is necessary to use a lower bound of search step size to avoid this problem. Unfortunately, the optimal setting of lower bound is highly problem dependent. This paper first analyzes both theoretically and empirically how the step size control was lost. Then two schemes of dynamic lower bound are proposed. The schemes enable the EP algorithm to adjust the lower bound dynamically during evolution. Experimental results are presented to demonstrate the effectiveness and efficiency of the dynamic lower bound for a set of benchmark functions.