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Open AccessFeature PaperArticle

Pool-Based Genetic Programming Using Evospace, Local Search and Bloat Control

1
Tecnológico Nacional de México/Instituto Tecnológico de Tijuana, Tijuana BC C.P. 22430, Mexico
2
Departamento de Tecnología de los Computadores y de las Comunicaciones, Universidad de Extremadura, 06800 Mérida, Spain
3
Departamento de Ingeniería Sistemas Informáticos y Telemáticos, Universidad de Extremadura, 06800 Mérida, Spain
*
Author to whom correspondence should be addressed.
Math. Comput. Appl. 2019, 24(3), 78; https://doi.org/10.3390/mca24030078
Received: 29 July 2019 / Revised: 27 August 2019 / Accepted: 27 August 2019 / Published: 29 August 2019
(This article belongs to the Special Issue Numerical and Evolutionary Optimization)
This work presents a unique genetic programming (GP) approach that integrates a numerical local search method and a bloat-control mechanism within a distributed model for evolutionary algorithms known as EvoSpace. The first two elements provide a directed search operator and a way to control the growth of evolved models, while the latter is meant to exploit distributed and cloud-based computing architectures. EvoSpace is a Pool-based Evolutionary Algorithm, and this work is the first time that such a computing model has been used to perform a GP-based search. The proposal was extensively evaluated using real-world problems from diverse domains, and the behavior of the search was analyzed from several different perspectives. The results show that the proposed approach compares favorably with a standard approach, identifying promising aspects and limitations of this initial hybrid system. View Full-Text
Keywords: Genetic Programming; Bloat; NEAT; Local Search; EvoSpace Genetic Programming; Bloat; NEAT; Local Search; EvoSpace
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MDPI and ACS Style

Juárez-Smith, P.; Trujillo, L.; García-Valdez, M.; Fernández de Vega, F.; Chávez, F. Pool-Based Genetic Programming Using Evospace, Local Search and Bloat Control. Math. Comput. Appl. 2019, 24, 78.

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