A steep thermodynamical selection rule for evolutionary algorithms

作者：Ying Weiqin^*; Li Yuanxiang; Peng Shujuan; Wang Weiwu

来源：7th International Conference on Computational Science, ICCS 2007, 2007-05-27 to 2007-05-30.

• Population diversity
• Selection rules
• Thermodynamical genetic algorithms (TDGA)

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).

• 出版日期2007