Optimization by searching a tree of populations
Louis I. Steinberg, Khaled Rasheed · 1999
GAs have been found to be useful in handling many numerical optimization problems. Because of the variability in results inherent in the stochastic nature of GAs, it is common to run a GA several times and take the best of the results. However, it is possible to save a GA’s population at some intermediate states and restart from one of these populations instead of from the very beginning. By doing so we generate a tree of populations, where a child population is generated from its parent by running some number of GA iterations. We describe two methods for searching such a tree of populations, one based on Highest Utility First Search (HUFS) and one that proceeds level by level with no backtracking, and give the results of testing them on a realworld optimization task involving conceptual design of supersonic transport aircraft. They both do much better than repeatedly running the GA from the beginning, with HUFS achieving equivalent results in less than half the GA iterations in some situations. 1