Author:

Keywords:

machine learning, genetic algorithms, evolutionary computation, protein structure prediction

Abstract:

In the Genetic Algorithm (GA) with the standard random immigrants approach, a fixed number of individuals of the current population are replaced by random individuals in every generation. The random immigrants inserted in every generation maintain, or increase, the diversity of the population, what is advantageous to GAs applied to complex problems like the protein structure prediction problem. The rate of replaced individuals in the standard random immigrants approach is defined a priori, and has a great influence on the performance of the algorithm. In this paper, we propose a new strategy to control the number of random immigrants in GAs, applied to the protein structure prediction problem. Instead of using a fixed number of new individuals per generation, the proposed approach alters the number of new individuals to be inserted in the generation according to a self-organizing process. Experimental results indicate that the performance of the proposed algorithm in the protein structure prediction problem is superior or similar to the performance of the standard random immigrants approach with the best rate of individual replacement.