Improving the Efficiency of Genetic Algorithms for Linearly Constrained Optimization

Abstract

The efficiency of a Genetic Algorithm for constrained parameter optimization depends heavily on the ratio of feasible to infeasible area in its rectangular search space. We show an algorithm based on existing mathematical programming methods which improves this ratio assuming a set of linear constraints. We approximate the feasible area by a multidimensional ellipsoid and rotate the original search space parallel to its main axes. The minimum volume hyper rectangle we can wrap around the rotated constraint set gives us a new rectangular search space. In addition to that we propose to continue with a local search algorithm for fine tuning. To demonstrate the use of the proposed method, we perform test runs on randomly generated cases as well as on three selected examples.