%0 Conference Paper %1 781913 %A Knowles, J. %A Corne, D. %B Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on %D 1999 %K *file-import-12-02-14 11, algorithmcandidate, algorithmlocal, algorithmpopulation-based, algorithms, archived, archivereproductiontelecommunicationstest, dominance, evolution, found, functionsgenetic, methodspreviously, multiobjective, optimal, optimisationpareto, rankingbaseline, searchnontrivial, setapproximate, solution, solutionsevolutionary, solutionsreference, strategypareto, vectorscurrent, vectorsdiverse, %R 10.1109/CEC.1999.781913 %T The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation %U http://dx.doi.org/10.1109/CEC.1999.781913 %V 1 %X Most popular evolutionary algorithms for multiobjective optimisation maintain a population of solutions from which individuals are selected for reproduction. In this paper, we introduce a simpler evolution scheme for multiobjective problems, called the Pareto archived evolution strategy (PAES). We argue that PAES may represent the simplest possible non-trivial algorithm capable of generating diverse solutions in the Pareto optimal set. The algorithm is identified as being a (1+1) evolution strategy, using local search from a population of one but using a reference archive of previously found solutions in order to identify the approximate dominance ranking of the current and candidate solution vectors. PAES is intended as a good baseline approach, against which more involved methods may be compared, and may also serve well in some real-world applications when local search seems superior to or competitive with population-based methods. The performance of the new algorithm is compared with that of a MOEA based on the niched Pareto GA on a real world application from the telecommunications field. In addition, we include results from experiments carried out on a suite of four test functions, to demonstrate the algorithm's general capability

@inproceedings{781913, abstract = {{Most popular evolutionary algorithms for multiobjective optimisation maintain a population of solutions from which individuals are selected for reproduction. In this paper, we introduce a simpler evolution scheme for multiobjective problems, called the Pareto archived evolution strategy (PAES). We argue that PAES may represent the simplest possible non-trivial algorithm capable of generating diverse solutions in the Pareto optimal set. The algorithm is identified as being a (1+1) evolution strategy, using local search from a population of one but using a reference archive of previously found solutions in order to identify the approximate dominance ranking of the current and candidate solution vectors. PAES is intended as a good baseline approach, against which more involved methods may be compared, and may also serve well in some real-world applications when local search seems superior to or competitive with population-based methods. The performance of the new algorithm is compared with that of a MOEA based on the niched Pareto GA on a real world application from the telecommunications field. In addition, we include results from experiments carried out on a suite of four test functions, to demonstrate the algorithm's general capability}}, added-at = {2012-03-02T03:39:18.000+0100}, author = {Knowles, J. and Corne, D.}, biburl = {https://www.bibsonomy.org/bibtex/2a7a5309f80759bc1e65f9191bba977b4/baby9992006}, booktitle = {Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on}, citeulike-article-id = {10349737}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/CEC.1999.781913}, doi = {10.1109/CEC.1999.781913}, interhash = {45218c4cad1a6d1f54a09594b4d5d421}, intrahash = {a7a5309f80759bc1e65f9191bba977b4}, keywords = {*file-import-12-02-14 11, algorithmcandidate, algorithmlocal, algorithmpopulation-based, algorithms, archived, archivereproductiontelecommunicationstest, dominance, evolution, found, functionsgenetic, methodspreviously, multiobjective, optimal, optimisationpareto, rankingbaseline, searchnontrivial, setapproximate, solution, solutionsevolutionary, solutionsreference, strategypareto, vectorscurrent, vectorsdiverse,}, posted-at = {2012-02-14 03:07:28}, priority = {2}, timestamp = {2012-03-02T03:39:24.000+0100}, title = {{The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation}}, url = {http://dx.doi.org/10.1109/CEC.1999.781913}, volume = 1, year = 1999 }