A Steep Thermodynamical Selection Rule for Evolutionary Algorithms

Authors:
Weiqin Ying;Yuanxiang Li;Shujuan Peng;Weiwu Wang
Affiliations:
State Key Lab. of Software Engineering, Wuhan University, Wuhan 430072, China;State Key Lab. of Software Engineering, Wuhan University, Wuhan 430072, China and School of Computer Science, Wuhan University, Wuhan 430079, China;School of Computer Science, Wuhan University, Wuhan 430079, China;State Key Lab. of Software Engineering, Wuhan University, Wuhan 430072, China
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Year:
2007

Citing 5
Cited 0

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Abstract

The genetic algorithm (GA) often suffers from the premature convergence because of the loss of population diversity at an early stage of searching. This paper proposes a steep thermodynamical evolutionary algorithm (STEA), which utilizes a steep thermodynamical selection (STS) rule. STEA simulates the competitive mechanism between energy and entropy in annealing to systematically resolve the conflicts between selective pressure and population diversity in GA. This paper also proves that the rule STS has the approximate steepest descent ability of the free energy. Experimental results show that STEA is both far more efficient and much stabler than the thermodynamical genetic algorithm (TDGA).