A self-tuning evolutionary algorithm applied to an inverse partial differential equation
Evolutionary algorithms ( EAs) are becoming increasingly popular tools for solving complex search problems. Their popularity in various problem domains has led to the introduction and development of numerous variants of two standard EA operators - crossover and mutation. Unfortunately, there are few if any effective guidelines for choosing which operators will be most effective in a given problem. In this paper, a self-tuning EA is introduced that employs several crossover and mutation operators simultaneously. The probability of using a given operator changes during the course of an evolutionary run whereby the most effective operators are selected based on which part of the search space is currently being explored. The self-tuning EA is used to solve an inverse partial differential equation - considered to be one of the more difficult problems in the realm of engineering mathematics. Results indicate that for the particular inverse partial differential equation considered, the self-tuning EA provides an effective solution methodology.
evolutionary algorithm partial differential equations inverse problems self-tuning algorithm