Applying evolutionary programming to selected control problems

Abstract

Evolutionary programming is a stochastic optimization procedure that can be applied to difficult combinatorial problems. Experiments are conducted with three standard optimal control problems (linear-quadratic, harvest, and push-cart). The results are compared to those obtained with genetic algorithms and the General Algebraic Modeling System (GAMS), a numerical optimization software package. The results indicate that evolutionary programming generally outperforms genetic algorithms. Evolutionary programming also compares well with GAMS on certain problems for which GAMS is specifically designed and outperforms GAMS on other problems. The computational requirements for each procedure are briefly discussed. © 1994.