Efficiency Enhancement of Genetic Algorithms via Building-Block-Wise Fitness Estimation

TL;DR

This paper proposes a fitness inheritance method for estimation distribution algorithms that uses building-block fitnesses to significantly reduce function evaluations and improve convergence speed.

Contribution

It introduces a novel fitness inheritance approach based on building-block fitness estimation, enhancing efficiency in genetic algorithms.

Findings

Reduces function evaluations by about 50% for additively separable problems.

Achieves a speed-up of approximately 1.75 to 2.25 times.

Provides a modeling framework for convergence time and population size effects.

Abstract

This paper studies fitness inheritance as an efficiency enhancement technique for a class of competent genetic algorithms called estimation distribution algorithms. Probabilistic models of important sub-solutions are developed to estimate the fitness of a proportion of individuals in the population, thereby avoiding computationally expensive function evaluations. The effect of fitness inheritance on the convergence time and population sizing are modeled and the speed-up obtained through inheritance is predicted. The results show that a fitness-inheritance mechanism which utilizes information on building-block fitnesses provides significant efficiency enhancement. For additively separable problems, fitness inheritance reduces the number of function evaluations to about half and yields a speed-up of about 1.75--2.25.