Entropy-Boltzmann selection in the genetic algorithms

A new selection method, entropy-Boltzmann selection, for genetic algorithms (GAs) is proposed. This selection method is based on entropy and importance sampling methods in Monte Carlo simulation. It naturally leads to adaptive fitness in which the fitness function does not stay fixed but varies with the environment. With the selection method, the algorithm can explore as many configurations as possible while exploiting better configurations, consequently helping to solve the premature convergence problem. To test the performance of the selection method, we use the NK-model and compared the performances of the proposed selection scheme with those of canonical GAs.