Sensitivity Analysis of a Genetic Algorithm for a Competitive Facility Location Problem Export

@incollection{citeulike:3135597, abstract = {The paper describes an application of a multi-objective evolutionary algorithm that finds Pareto-optimal solutions to a competitive facility location problem and reports a sensitivity analysis of the evolutionary algorithm model. A genetic algorithm which uses non-uniform mutation and continuous recombination is developed to find solutions to the bi-objective location problem. Results show that the genetic algorithm can find solutions close to the Pareto-optimal set in polynomial time and results from sensitivity analysis show that the mutation rate has the largest influence on the output among the input factors.}, author = {Pangilinan, Jos\'{e} M. and Janssens, Gerrit K. and Caris, An}, citeulike-article-id = {3135597}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-1-4020-8735-6_50}, doi = {10.1007/978-1-4020-8735-6_50}, journal = {Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering}, keywords = {continuous, evolutionary-computation, genetic-algorithm, multi-objective, parameter-setting, performance-assessment}, pages = {266--271}, posted-at = {2008-08-19 08:49:59}, priority = {2}, title = {Sensitivity Analysis of a Genetic Algorithm for a Competitive Facility Location Problem}, url = {http://dx.doi.org/10.1007/978-1-4020-8735-6_50}, year = {2008} }

TY - CHAP ID - citeulike:3135597 L3 - citeulike-article-id:3135597 N2 - The paper describes an application of a multi-objective evolutionary algorithm that finds Pareto-optimal solutions to a competitive facility location problem and reports a sensitivity analysis of the evolutionary algorithm model. A genetic algorithm which uses non-uniform mutation and continuous recombination is developed to find solutions to the bi-objective location problem. Results show that the genetic algorithm can find solutions close to the Pareto-optimal set in polynomial time and results from sensitivity analysis show that the mutation rate has the largest influence on the output among the input factors. JF - Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering EP - 271 TI - Sensitivity Analysis of a Genetic Algorithm for a Competitive Facility Location Problem SP - 266 KW - continuous KW - evolutionary-computation KW - genetic-algorithm KW - multi-objective KW - parameter-setting KW - performance-assessment AU - Pangilinan, José AU - Janssens, Gerrit AU - Caris, An PY - 2008/// UR - http://dx.doi.org/10.1007/978-1-4020-8735-6_50 ER -