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Publicação
Full Inclusive Genetic Programming
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Orientador(es)
Resumo(s)
This manuscript presents an improved version of the Inclusive Genetic Programming (IGP) algorithm. The IGP was developed to promote and maintain the population's genotypic diversity and showed superior performance compared to standard Genetic Programming (GP). In this work, two modifications to the IGP are proposed: first, the diversity promotion and maintenance mechanism is enhanced with information from the phenotype of the individuals rather than only the genotype; second, the Evolutionary Demes Despeciation Algorithm - V2 (EDDA-V2) is used to initialize the population. The phenotype is considered to differentiate the individuals also according to their behaviour rather than only their structure, while EDDA-V2 is employed to start the evolution with a simultaneously diverse and fit population, contrary to traditional initialization techniques. The algorithms incorporating these improvements are called Full Inclusive Genetic Programming (FIGP) and FIGP _E, respectively with and without the EDDA-V2 initialization. The experimental results, performed over eight benchmarks and considering six algorithms, demonstrate the superior performance of FIGP and FIGP _E in comparison to other GP formulations. Moreover, the EDDA-V2 initialization allows for a significant reduction of the computational time.
Descrição
Marchetti, F., Castelli, M., Bakurov, I., & Vanneschi, L. (2024). Full Inclusive Genetic Programming. In 2024 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CEC60901.2024.10611808 --- This work was partially supported by FCT, Portugal, through funding of research unit MagIC/NOVA IMS (UIDB/04152/2020); and by the SPECIES Society through the SPECIES Scholarship 2022.
Palavras-chave
Genetic Programming Population’s Diversity Symbolic Regression PMLB Benchmarks Population Initialization Artificial Intelligence Computer Science Applications Computer Vision and Pattern Recognition Computational Mathematics Control and Optimization
Contexto Educativo
Citação
Editora
Institute of Electrical and Electronics Engineers (IEEE)