2014/07/27 by João Pedro Pedroso, Pedroso, João Pedro
Computer Science · Engineering · Mathematics · #80M50 #Advanced Multi-Objective Optimization Algorithms #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Mathematics #G.1.6 #I.2.8 #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #Optimization and Control (math.OC) #Vehicle Routing Optimization Methods #acm:80M50 #cs.AI #cs.NE #math.OC #msc:80M50
paper · pdf · doi:10.48550/arxiv.1407.7211
15 pages
arxiv created 2014/07/27 · openalex publication_date 2014/07/27 · arxiv updated 2014/07/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper we introduce an evolutionary algorithm for the solution of linear integer programs. The strategy is based on the separation of the variables into the integer subset and the continuous subset; the integer variables are fixed by the evolutionary system, and the continuous ones are determined in function of them, by a linear program solver. We report results obtained for some standard benchmark problems, and compare them with those obtained by branch-and-bound. The performance of the evolutionary algorithm is promising. Good feasible solutions were generally obtained, and in some of the difficult benchmark tests it outperformed branch-and-bound.