2020 |
Luque, Mariano; Gonzalez-Gallardo, Sandra; Saborido, Rubén; Ruiz, Ana B Adaptive Global WASF-GA to handle many-objective optimization problems Journal Article Swarm and Evolutionary Computation, 54 (100644), 2020, ISSN: 2210-6502. Abstract | Links | BibTeX | Tags: Achievement scalarizing function, evolutionary algorithm, Many-objective optimization, Pareto optimal solutions, Weight vectors @article{luque_adaptive_2020, title = {Adaptive Global WASF-GA to handle many-objective optimization problems}, author = {Mariano Luque and Sandra Gonzalez-Gallardo and Rubén Saborido and Ana B Ruiz}, url = {http://www.sciencedirect.com/science/article/pii/S2210650218306187}, doi = {https://doi.org/10.1016/j.swevo.2020.100644}, issn = {2210-6502}, year = {2020}, date = {2020-05-01}, journal = {Swarm and Evolutionary Computation}, volume = {54}, number = {100644}, abstract = {In this paper, a new version of the aggregation-based evolutionary algorithm Global WASF-GA (GWASF-GA) for many-objective optimization is proposed, called Adaptive Global WASF-GA (A-GWASF-GA). The fitness function of GWASF-GA is defined by an achievement scalarizing function (ASF) based on the Tchebychev distance, which considers two reference points (the nadir and utopian points) and a set of weight vectors. Despite of the benefits of using these two reference points simultaneously and a well-distributed set of weight vectors, it is necessary to go a step further to get better approximations in problems with complicated Pareto optimal fronts. For this, in A-GWASF-GA, some of the weight vectors are re-calculated during the optimization process based on the sparsity of the solutions found so far, and taking into account some theoretical results demonstrated in this paper regarding the ASF considered. Different strategies are carried out to accelerate the convergence and to maintain the diversity. The computational results, carried out in comparison with RVEA, NSGA-III, and different versions of MOEA/D, show the potential of A-GWASF-GA in well-known but also in novel many-objective optimization benchmark problems.}, keywords = {Achievement scalarizing function, evolutionary algorithm, Many-objective optimization, Pareto optimal solutions, Weight vectors}, pubstate = {published}, tppubtype = {article} } In this paper, a new version of the aggregation-based evolutionary algorithm Global WASF-GA (GWASF-GA) for many-objective optimization is proposed, called Adaptive Global WASF-GA (A-GWASF-GA). The fitness function of GWASF-GA is defined by an achievement scalarizing function (ASF) based on the Tchebychev distance, which considers two reference points (the nadir and utopian points) and a set of weight vectors. Despite of the benefits of using these two reference points simultaneously and a well-distributed set of weight vectors, it is necessary to go a step further to get better approximations in problems with complicated Pareto optimal fronts. For this, in A-GWASF-GA, some of the weight vectors are re-calculated during the optimization process based on the sparsity of the solutions found so far, and taking into account some theoretical results demonstrated in this paper regarding the ASF considered. Different strategies are carried out to accelerate the convergence and to maintain the diversity. The computational results, carried out in comparison with RVEA, NSGA-III, and different versions of MOEA/D, show the potential of A-GWASF-GA in well-known but also in novel many-objective optimization benchmark problems. |
2018 |
Stolfi, Daniel H; Cintrano, Christian; Chicano, Francisco; Alba, Enrique Natural Evolution Tells Us How to Best Make Goods Delivery: Use Vans Inproceedings Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 308–309, ACM, Kyoto, Japan, 2018, ISBN: 978-1-4503-5764-7. Links | BibTeX | Tags: application, city policy, evolutionary algorithm, real world, road traffic, smart mobility @inproceedings{Stolfi:2018:NET:3205651.3205764, title = {Natural Evolution Tells Us How to Best Make Goods Delivery: Use Vans}, author = {Daniel H Stolfi and Christian Cintrano and Francisco Chicano and Enrique Alba}, doi = {10.1145/3205651.3205764}, isbn = {978-1-4503-5764-7}, year = {2018}, date = {2018-01-01}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion}, pages = {308--309}, publisher = {ACM}, address = {Kyoto, Japan}, series = {GECCO '18}, keywords = {application, city policy, evolutionary algorithm, real world, road traffic, smart mobility}, pubstate = {published}, tppubtype = {inproceedings} } |
2012 |
Ferrer, Javier; Kruse, Peter M; Chicano, Francisco; Alba, Enrique Evolutionary algorithm for prioritized pairwise test data generation Inproceedings Soule, Terence; Moore, Jason H (Ed.): łdots on Genetic and evolutionary łdots, pp. 1213–1220, ACM, New York, New York, USA, 2012, ISBN: 978-1-4503-1177-9. Abstract | Links | BibTeX | Tags: combinatorial testing, evolutionary algorithm, pair-, pairwise coverage, prioritization, search based soft-, search based software engineering, software testing, Testing Funcional, ware engineering @inproceedings{DBLP:conf/gecco/FerrerKCA12, title = {Evolutionary algorithm for prioritized pairwise test data generation}, author = {Javier Ferrer and Peter M Kruse and Francisco Chicano and Enrique Alba}, editor = {Terence Soule and Jason H Moore}, url = {http://dl.acm.org/citation.cfm?id=2330163.2330331 http://dl.acm.org/citation.cfm?doid=2330163.2330331 http://dl.acm.org/citation.cfm?id=2330331}, doi = {10.1145/2330163.2330331}, isbn = {978-1-4503-1177-9}, year = {2012}, date = {2012-07-01}, booktitle = {łdots on Genetic and evolutionary łdots}, pages = {1213--1220}, publisher = {ACM}, address = {New York, New York, USA}, abstract = {Combinatorial Interaction Testing (CIT) is a technique used to discover faults caused by parameter interactions in highly configurable systems. These systems tend to be large and exhaustive testing is generally impractical. Indeed, when the resources are limited, prioritization of test cases is a must. Important test cases are assigned a high priority and should be executed earlier. On the one hand, the prioritization of test cases may reveal faults in early stages of the testing phase. But, on the other hand the generation of minimal test suites that fulfill the demanded coverage criteria is an NP-hard problem. Therefore, search based approaches are required to find the (near) optimal test suites. In this work we present a novel evolutionary algorithm to deal with this problem. The experimental analysis compares five techniques on a set of benchmarks. It reveals that the evolutionary approach is clearly the best in our comparison. The presented algorithm can be integrated into CTE XL professional tool.}, keywords = {combinatorial testing, evolutionary algorithm, pair-, pairwise coverage, prioritization, search based soft-, search based software engineering, software testing, Testing Funcional, ware engineering}, pubstate = {published}, tppubtype = {inproceedings} } Combinatorial Interaction Testing (CIT) is a technique used to discover faults caused by parameter interactions in highly configurable systems. These systems tend to be large and exhaustive testing is generally impractical. Indeed, when the resources are limited, prioritization of test cases is a must. Important test cases are assigned a high priority and should be executed earlier. On the one hand, the prioritization of test cases may reveal faults in early stages of the testing phase. But, on the other hand the generation of minimal test suites that fulfill the demanded coverage criteria is an NP-hard problem. Therefore, search based approaches are required to find the (near) optimal test suites. In this work we present a novel evolutionary algorithm to deal with this problem. The experimental analysis compares five techniques on a set of benchmarks. It reveals that the evolutionary approach is clearly the best in our comparison. The presented algorithm can be integrated into CTE XL professional tool. |
2009 |
Ferrer, Javier; Chicano, Francisco; Alba, Enrique Dealing with inheritance in OO evolutionary testing Inproceedings Genetic and evolutionary computation - GECCO '09, pp. 1665, ACM Press, New York, USA, 2009, ISBN: 9781605583259. Links | BibTeX | Tags: evolutionary algorithm, instanceof, object-oriented, oo evolutionary testing, search based software engineering, software testing @inproceedings{Ferrer2009a, title = {Dealing with inheritance in OO evolutionary testing}, author = {Javier Ferrer and Francisco Chicano and Enrique Alba}, url = {http://dl.acm.org/citation.cfm?id=1569901.1570124}, doi = {10.1145/1569901.1570124}, isbn = {9781605583259}, year = {2009}, date = {2009-07-01}, booktitle = {Genetic and evolutionary computation - GECCO '09}, pages = {1665}, publisher = {ACM Press}, address = {New York, USA}, keywords = {evolutionary algorithm, instanceof, object-oriented, oo evolutionary testing, search based software engineering, software testing}, pubstate = {published}, tppubtype = {inproceedings} } |
2020 |
Adaptive Global WASF-GA to handle many-objective optimization problems Journal Article Swarm and Evolutionary Computation, 54 (100644), 2020, ISSN: 2210-6502. |
2018 |
Natural Evolution Tells Us How to Best Make Goods Delivery: Use Vans Inproceedings Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 308–309, ACM, Kyoto, Japan, 2018, ISBN: 978-1-4503-5764-7. |
2012 |
Evolutionary algorithm for prioritized pairwise test data generation Inproceedings Soule, Terence; Moore, Jason H (Ed.): łdots on Genetic and evolutionary łdots, pp. 1213–1220, ACM, New York, New York, USA, 2012, ISBN: 978-1-4503-1177-9. |
2009 |
Dealing with inheritance in OO evolutionary testing Inproceedings Genetic and evolutionary computation - GECCO '09, pp. 1665, ACM Press, New York, USA, 2009, ISBN: 9781605583259. |