Towards improvement of SUNA in multiplexers with preliminary results of simple logic gate neuron variation

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

Abstract

Spectrum-Diverse Neuroevolution with Unified Neural Models (SUNA) has been shown to be a successful alternative to the algorithm NeuroEvolution of Augmenting Topologies (NEAT). Requiring less parameters than NEAT yet possessing a more unified representation power and effective spectrum-based diversity preservation, SUNA outperformed NEAT on most of the problems to be experimented. However, we think a simple improvement approach can be made to improve SUNA's efficiency in the strategic decision-making problem tested by the model itself, i.e. the multiplexer problem. In the proposed method, we try to incorporate the idea of logical gates to the hidden neurons in the model, suggesting it the solutions that solve the problem in the real world in the form of neurons. It is shown that with the simple logic gates neuron variations, SUNA can be slightly enhanced to resolve the multiplexer problem.

Original languageEnglish
Title of host publicationGECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages53-54
Number of pages2
ISBN (Electronic)9781450371278
DOIs
Publication statusPublished - Jul 8 2020
Event2020 Genetic and Evolutionary Computation Conference, GECCO 2020 - Cancun, Mexico
Duration: Jul 8 2020Jul 12 2020

Publication series

NameGECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2020 Genetic and Evolutionary Computation Conference, GECCO 2020
CountryMexico
CityCancun
Period7/8/207/12/20

All Science Journal Classification (ASJC) codes

  • Computational Mathematics

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    Ta, A. D., & Vargas, D. V. (2020). Towards improvement of SUNA in multiplexers with preliminary results of simple logic gate neuron variation. In GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 53-54). (GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery, Inc. https://doi.org/10.1145/3377929.3398164