2017/02/12 by Andrés Herrera-Poyatos, Francisco Herrera, Herrera-Poyatos, Andrés +1
Computer Science · #Advanced Multi-Objective Optimization Algorithms #Artificial Intelligence (cs.AI) #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #I.2.8 #Metaheuristic Optimization Algorithms Research
paper · pdf · doi:10.48550/arxiv.1702.03594
openalex publication_date 2017/02/12 · openalex created_date 2022/09/03 · openalex updated_date 2026/07/28
The lack of diversity in a genetic algorithm's population may lead to a bad\nperformance of the genetic operators since there is not an equilibrium between\nexploration and exploitation. In those cases, genetic algorithms present a fast\nand unsuitable convergence.\n In this paper we develop a novel hybrid genetic algorithm which attempts to\nobtain a balance between exploration and exploitation. It confronts the\ndiversity problem using the named greedy diversification operator. Furthermore,\nthe proposed algorithm applies a competition between parent and children so as\nto exploit the high quality visited solutions. These operators are complemented\nby a simple selection mechanism designed to preserve and take advantage of the\npopulation diversity.\n Additionally, we extend our proposal to the field of memetic algorithms,\nobtaining an improved model with outstanding results in practice.\n The experimental study shows the validity of the approach as well as how\nimportant is taking into account the exploration and exploitation concepts when\ndesigning an evolutionary algorithm.\n