TY - GEN
T1 - PSXO
T2 - 2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017
AU - Vanneschi, Leonardo
AU - Castelli, Mauro
AU - Moraglio, Alberto
AU - Manzoni, Luca
AU - Silva, Sara
AU - Krawiec, Krzysztof
AU - Gonçalves, Ivo
N1 - Vanneschi, Leonardo ; Castelli, Mauro ; Moraglio, Alberto ; Manzoni, Luca ; Silva, Sara ; Krawiec, Krzysztof ; Gonçalves, Ivo. / PSXO : Population-wide semantic crossover. GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, 2017. pp. 257-258 (GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion).
PY - 2017/7/15
Y1 - 2017/7/15
N2 - Since its introduction, Geometric Semantic Genetic Programming (GSGP) has been the inspiration to ideas on how to reach optimal solutions eciently. Among these, in 2016 Pawlak has shown how to analytically construct optimal programs by means of a linear combination of a set of random programs. Given the simplicity and excellent results of this method (LC) when compared to GSGP, the author concluded that GSGP is “overkill”. However, LC has limitations, and it was tested only on simple benchmarks. In this paper, we introduce a new method, Population-Wide Semantic Crossover (PSXO), also based on linear combinations of random programs, that overcomes these limitations. We test the rst variant (Inv) on a diverse set of complex real-life problems, comparing it to LC, GSGP and standard GP. We realize that, on the studied problems, both LC and Inv are outperformed by GSGP, and sometimes also by standard GP. is leads us to the conclusion that GSGP is not overkill. We also introduce a second variant (GPinv) that integrates evolution with the approximation of optimal programs by means of linear combinations. GPinv outperforms both LC and Inv on unseen test data for the studied problems.
AB - Since its introduction, Geometric Semantic Genetic Programming (GSGP) has been the inspiration to ideas on how to reach optimal solutions eciently. Among these, in 2016 Pawlak has shown how to analytically construct optimal programs by means of a linear combination of a set of random programs. Given the simplicity and excellent results of this method (LC) when compared to GSGP, the author concluded that GSGP is “overkill”. However, LC has limitations, and it was tested only on simple benchmarks. In this paper, we introduce a new method, Population-Wide Semantic Crossover (PSXO), also based on linear combinations of random programs, that overcomes these limitations. We test the rst variant (Inv) on a diverse set of complex real-life problems, comparing it to LC, GSGP and standard GP. We realize that, on the studied problems, both LC and Inv are outperformed by GSGP, and sometimes also by standard GP. is leads us to the conclusion that GSGP is not overkill. We also introduce a second variant (GPinv) that integrates evolution with the approximation of optimal programs by means of linear combinations. GPinv outperforms both LC and Inv on unseen test data for the studied problems.
KW - Inverse matrix
KW - Population-wide crossover
KW - Real-life problems
KW - Semantics
UR - http://www.scopus.com/inward/record.url?scp=85046763773&partnerID=8YFLogxK
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000625865500129
U2 - 10.1145/3067695.3076003
DO - 10.1145/3067695.3076003
M3 - Conference contribution
AN - SCOPUS:85046763773
T3 - GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion
SP - 257
EP - 258
BT - GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion
PB - Association for Computing Machinery, Inc
Y2 - 15 July 2017 through 19 July 2017
ER -