Abstract
This work presents a performance analysis of a Multi-Branches Genetic Programming (MBGP) approach applied in symbolic regression (e.g. function approximation) problems. Genetic Programming (GP) has been previously applied to this kind of regression. However, one of the main drawbacks of GP is the fact that individuals tend to grow in size through the evolution process without a significant improvement in individual performance. In Multi-Branches Genetic Programming (MBGP), an individual is composed of several branches, each branch can evolve a part of individual solution, and final solution is composed of the integration of these partial solutions.
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Streeter, M., Becker, L.A.: Automated Discovery of Numerical Approximation Formulae via Genetic Programming. Genetic Programming and Evolvable Machines 4(3), 255–286 (2003)
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© 2004 Springer-Verlag Berlin Heidelberg
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Rodríguez-Vázquez, K., Oliver-Morales, C. (2004). Multi-branches Genetic Programming as a Tool for Function Approximation. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_85
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DOI: https://doi.org/10.1007/978-3-540-24855-2_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22343-6
Online ISBN: 978-3-540-24855-2
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