2015/12/07 by Duc-Cuong Dang, Anton V. Eremeev, Dang, Duc-Cuong +3
Computer Science · Engineering · #Advanced Control Systems Optimization #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #Scheduling and Optimization Algorithms
paper · pdf · doi:10.48550/arxiv.1512.02047
openalex publication_date 2015/12/07 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28
The paper is devoted to upper bounds on run-time of Non-Elitist Genetic\nAlgorithms until some target subset of solutions is visited for the first time.\nIn particular, we consider the sets of optimal solutions and the sets of local\noptima as the target subsets. Previously known upper bounds are improved by\nmeans of drift analysis. Finally, we propose conditions ensuring that a\nNon-Elitist Genetic Algorithm efficiently finds approximate solutions with\nconstant approximation ratio on the class of combinatorial optimization\nproblems with guaranteed local optima (GLO).\n