Galvan, Edgar and Schoenauer, Marc (2019) Promoting semantic diversity in multi-objective genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '19). ACM, pp. 1021-1029. ISBN 9781450361118

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Abstract

The study of semantics in Genetic Programming (GP) has increased dramatically over the last years due to the fact that researchers tend to report a performance increase in GP when semantic diversity is promoted. However, the adoption of semantics in Evolutionary Multi-objective Optimisation (EMO), at large, and in Multi-objective GP (MOGP), in particular, has been very limited and this paper intends to fill this challenging research area. We propose a mechanism wherein a semantic-based distance is used instead of the widely known crowding distance and is also used as an objective to be optimised. To this end, we use two well-known EMO algorithms: NSGA-II and SPEA2. Results on highly unbalanced binary classification tasks indicate that the proposed approach produces more and better results than the rest of the three other approaches used in this work, including the canonical aforementioned EMO algorithms.

Item Type:	Book Section
Additional Information:	Cite as: Edgar Galván and Marc Schoenauer. 2019. Promoting semantic diversity in multi-objective genetic programming. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '19). Association for Computing Machinery, New York, NY, USA, 1021–1029. DOI:https://doi.org/10.1145/3321707.3321854
Keywords:	Multi-objective Genetic Programming; Semantics;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	14365
Identification Number:	https://doi.org/10.1145/3321707.3321854
Depositing User:	Edgar Galvan
Date Deposited:	22 Apr 2021 13:36
Publisher:	ACM
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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