Batch tournament selection for genetic programming

Vinícius V. De Melo, Danilo Vasconcellos Vargas, Wolfgang Banzhaf

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Lexicase selection achieves very good solution quality by introducing ordered test cases. However, the computational complexity of lexicase selection can prohibit its use in many applications. In this paper, we introduce Batch Tournament Selection (BTS), a hybrid of tournament and lexicase selection which is approximately one order of magnitude faster than lexicase selection while achieving a competitive quality of solutions. Tests on a number of regression datasets show that BTS compares well with lexicase selection in terms of mean absolute error while having a speed-up of up to 25 times. Surprisingly, BTS and lexicase selection have almost no difference in both diversity and performance. This reveals that batches and ordered test cases are completely different mechanisms which share the same general principle fostering the specialization of individuals. This work introduces an efficient algorithm that sheds light onto the main principles behind the success of lexicase, potentially opening up a new range of possibilities for algorithms to come.

Original languageEnglish
Title of host publicationGECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery, Inc
Pages994-1002
Number of pages9
ISBN (Electronic)9781450361118
DOIs
Publication statusPublished - Jul 13 2019
Event2019 Genetic and Evolutionary Computation Conference, GECCO 2019 - Prague, Czech Republic
Duration: Jul 13 2019Jul 17 2019

Publication series

NameGECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference

Conference

Conference2019 Genetic and Evolutionary Computation Conference, GECCO 2019
CountryCzech Republic
CityPrague
Period7/13/197/17/19

Fingerprint

Genetic programming
Tournament
Genetic Programming
Batch
Computational complexity
Specialization
Computational Complexity
Speedup
Efficient Algorithms
Regression

All Science Journal Classification (ASJC) codes

  • Computational Mathematics

Cite this

De Melo, V. V., Vargas, D. V., & Banzhaf, W. (2019). Batch tournament selection for genetic programming. In GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 994-1002). (GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/3321707.3321793

Batch tournament selection for genetic programming. / De Melo, Vinícius V.; Vargas, Danilo Vasconcellos; Banzhaf, Wolfgang.

GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, Inc, 2019. p. 994-1002 (GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

De Melo, VV, Vargas, DV & Banzhaf, W 2019, Batch tournament selection for genetic programming. in GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference. GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, Inc, pp. 994-1002, 2019 Genetic and Evolutionary Computation Conference, GECCO 2019, Prague, Czech Republic, 7/13/19. https://doi.org/10.1145/3321707.3321793
De Melo VV, Vargas DV, Banzhaf W. Batch tournament selection for genetic programming. In GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, Inc. 2019. p. 994-1002. (GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference). https://doi.org/10.1145/3321707.3321793
De Melo, Vinícius V. ; Vargas, Danilo Vasconcellos ; Banzhaf, Wolfgang. / Batch tournament selection for genetic programming. GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, Inc, 2019. pp. 994-1002 (GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference).
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