Replacement strategies to preserve useful diversity in steady-state genetic algorithms
Information Sciences: an International Journal
A novel and accelerated genetic algorithm
WSEAS Transactions on Systems and Control
Conservation of information in search: measuring the cost of success
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
To explore or to exploit: An entropy-driven approach for evolutionary algorithms
International Journal of Knowledge-based and Intelligent Engineering Systems
Evolutionary computation and its applications in neural and fuzzy systems
Applied Computational Intelligence and Soft Computing
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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.