A global inferior-elimination thermodynamics selection strategy for evolutionary algorithm

Authors:
Fahong Yu;Yuanxiang Li;Weiqin Ying
Affiliations:
State Key Lab of Software Engineering, Wuhan University, Wuhan, China;State Key Lab of Software Engineering, Wuhan University, Wuhan, China;State Key Lab of Software Engineering, Wuhan University, Wuhan, China
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Year:
2010

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Abstract

In this paper, a new evolutionary algorithm based on global inferior-elimination thermodynamics selection strategy (IETEA) is proposed Also, a definition of the two-dimensional entropy (2D entropy) of the particle system is given and the law of entropy increment is applied to control the algorithm running The purpose of the new algorithm is to systematically harmonize the conflict between selective pressure and population diversity while searching for the optimal solutions The new algorithm conforms to the principle of minimal free energy in simulating the competitive mechanism between energy and entropy in annealing process By solving some typical high-dimension problems with multiple local optimizations, satisfactory results are achieved The results show that this algorithm has preferable capability to avoid the premature convergence effectively and reduce the cost in search to some extent.