Evolutionary optimization algorithm by entropic sampling
Chang‐Yong Lee, Seung Kee Han · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · 1998
A combinatorial optimization algorithm, genetic-entropic algorithm, is proposed. This optimization algorithm is based on the genetic algorithms and the natural selection via entropic sampling. With the entropic sampling, this algorithm helps to escape local optima in the complex optimization problems. To test the performance of the algorithm, we adopt the $\mathrm{NK}$ model ($N$ is the number of bits in the string and $K$ is the degree of epistasis) and compare the performances of the proposed algorithm with those of the canonical genetic algorithm. It is found that the higher the $K$ value, the better this algorithm can escape local optima and search near global optimum. The characteristics of this algorithm in terms of the power spectrum analysis together with the difference between two algorithms are discussed.