Compressed Introns in a Linkage Learning Genetic Algorithm
Fernando Graça Lobo, Kalyanmoy Deb, David E. Goldberg, Georges R. Harik, Liwei Wang · 1998
Over the last 10 years, many efforts have been made to design a competent genetic algorithm. This paper revisits and extends the latest of such efforts--- the linkage learning genetic algorithm. Specifically, it introduces an efficient mechanism for representing the noncoding material. Recent investigations suggest that this new method is crucial for solving a large class of hard optimization problems. 1 Introduction The simple genetic algorithm (SGA) has been applied successfully in a variety of applications, including medical, financial, and all kinds of engineering problems. Its power comes from its ability to combine good pieces (building blocks) from different solutions and assemble them into a single super solution. But despite their success, there are still problems whose solution can be constructed by the juxtaposition of building blocks, and yet the SGA fails. The reason behind this failure is well understood and is due to the socalled linkage problem. Before applying a ge...