A cascaded genetic algorithm for efficient optimization and pattern matching

摘要

A modified genetic algorithm (GA) based search strategy is presented here that is computationally more efficient than the conventional GA. Here the idea is to start a AG with the chromosomes of small length. Such chromosomes represent possible solutions with coarse Resolution. A finite space around the position of solution in the first stage is subject to the AG at the second state. Since this space is smaller Than the original search space, chromosomes of same length now represent finer resolution. In this way, the search progresses from coarse to Fine solution in a cascaded manner.