Illinois Genetic Algorithms Laboratory

Harik, G., Lobo, F., and Goldberg, D.E. (1997)
TR No.: 97006 | Download PDF | Download PS

Abstract:

This paper introduces the compact genetic algorithm (cGA). The cGA represents the population as a probability distribution over the set of solutions, and is operationally equivalent to the order-one behavior of the simple GA with uniform crossover. It processes each gene independently and requires less memory than the simple GA. Therefore, it can be used to give a quick estimate of a problem’s difficulty.

In addition, this work raises important questions about the use of information in a genetic algorithm, and its ramifications show us a direction that can lead to the design of more efficient GAs.