Description: Thermodynamical genetic algorithms(TDGA)simulate the competitive model between energy and
entropy in annealing to harmonize the conflicts between selective pressure and population diversity in GA．But high
computational cost restricts the applications of TDGA．In order to improve the computational efi ciency，a
measurement method of rating—based entropy(RE)is proposed．The RE method can measure the fitness dispersal
with low computational cost．Then a component therm odynamical replacement(CTR)rule is introduced to reduce
the complexity of the replacement，and it is proved that the CTR rule has the approximate steepest descent ability of
the population free energy．Experimental results on 0-1 knapsack problems show that the RE method and the CTR
rule not only maintain the excellent perform ance and stability of TDGA，but also remarkably improve the
computational efi ciency of TDGA．
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热力学遗传算法计算效率的改进实现.PDF
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