An Evolutionary Algorithm for Minimizing Multimodal Functions

A new evolutionary algorithm for the global optimization of multimodal functions is presented. The algorithm is essentially a parallel direct search method which maintains a populations of individuals and utilizes an evolution operator to evolve them. This operator has two functions. Firstly, to exploit the search space as much as possible, and secondly to form an improved population for the next generation. This operator makes the algorithm to rapidly converge to the global optimum of various difficult multimodal test functions.