Genetic and Memetic Algorithm with Diversity Equilibrium based on Greedy Diversification
This work addresses a common issue in evolutionary algorithms for optimization problems, but it is incremental as it builds on existing genetic and memetic algorithm frameworks.
The paper tackles the problem of premature convergence in genetic algorithms due to insufficient population diversity by introducing a hybrid genetic algorithm with a greedy diversification operator and a competition mechanism to balance exploration and exploitation. The experimental results demonstrate the validity of the approach, showing outstanding performance in practice.
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.