Abstract
This paper examines phenotype and genotype mappings that are biologically inspired. These types of coding are used in evolutionary computation. Direct and indirect encoding are studied. The determination of genotype and phenotype relationships and the connection to genetic algorithms, evolutionary programming and biology are examined in the light of newer advances. The NEAT and HyperNEAT algorithms are applied to the 2D Walker [41] problem of an agent learning how to walk. Results and findings are discussed, and conclusions are given. Indirect coding did not improve the situation. This paper shows that indirect coding is not useful in every situation.
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Acknowledgments
This work was supported by financial support of research project NPU I No. MSMT-7778/2014 by the Ministry of Education of the Czech Republic, by the European Regional Development Fund under the Project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089 and by resources of A.I. Lab research group at Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz).
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Meli, C., Nezval, V., Oplatkova, Z.K., Buttigieg, V., Staines, A.S. (2021). A Study of Direct and Indirect Encoding in Phenotype-Genotype Relationships. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2021. Lecture Notes in Computer Science(), vol 12855. Springer, Cham. https://doi.org/10.1007/978-3-030-87897-9_27
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