An entropy-based multi-population genetic algorithm: I. The basic principles

An improved genetic algorithm based on information entropy is presented in this paper. As a new iteration scheme in conjunction with multi-population genetic strategy, entropy-based searching technique with narrowing down space and the quasi-exact penalty function is developed to solve nonlinear programming (NLP) problems with equality and inequality constraints. A specific strategy of reserving the fittest member with evolutionary historic information is effectively used to approximate the solution of the nonlinear programming problems to the global optimization. Numerical examples show that the proposed method has good accuracy and efficiency.