Adapting Search Strategies to Induced Fitness Landscapes.
Terry P. Riopka · 2002
A new paradigm for genetic search referred to as the Collective Learning Genetic Algorithm (CLGA) has been demonstrated for combinatorial optimization problems which utilizes genotypic learning to do recombination based on a cooperative exchange of knowledge between interacting chromosomes. Recent evidence suggests that the success of the CLGA is not due to a capacity to do linkage learning, but due to the CLGA's high resistance to convergence and its ability to modify its recombinative behavior based on the consistency of the information in its environment, specifically, the observed fitness landscape. By analyzing the structure of the evolving individuals, a problem-difficulty metric is extracted a posteriori and then plotted for various types of example problems. This paper presents results that show that the CLGA chooses a search strategy appropriate to the fitness landscape induced by the CLGA itself. This is reflected in an empirical measurement of problem difficulty that is a natural byproduct of CLGA search.