Genetic and Memetic Algorithm with Diversity Equilibrium based on Greedy Diversification

TL;DR

This paper introduces a hybrid genetic algorithm with a greedy diversification operator and a competition mechanism to balance exploration and exploitation, extending it to memetic algorithms with promising practical results.

Contribution

It presents a novel hybrid genetic algorithm incorporating greedy diversification and competition strategies, extended to memetic algorithms, to improve diversity and performance.

Findings

Effective balance between exploration and exploitation achieved

Outperforms traditional genetic algorithms in experiments

Diverse solutions maintained throughout the process

Abstract

The lack of diversity in a genetic algorithm's population may lead to a bad performance of the genetic operators since there is not an equilibrium between exploration and exploitation. In those cases, genetic algorithms present a fast and unsuitable convergence. In this paper we develop a novel hybrid genetic algorithm which attempts to obtain a balance between exploration and exploitation. It confronts the diversity problem using the named greedy diversification operator. Furthermore, the proposed algorithm applies a competition between parent and children so as to exploit the high quality visited solutions. These operators are complemented by a simple selection mechanism designed to preserve and take advantage of the population diversity. Additionally, we extend our proposal to the field of memetic algorithms, obtaining an improved model with outstanding results in practice. The experimental study shows the validity of the approach as well as how important is taking into account the exploration and exploitation concepts when designing an evolutionary algorithm.