A (\mu + \lambda) - GP Algorithm and its use for Regression Problems

Authors:
Eduardo Oliveira Costa;Aurora Pozo
Affiliations:
Federal University of Parana (UFPR), Brazil;Federal University of Parana (UFPR), Brazil
Venue:
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Year:
2006

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Abstract

The Genetic Programming (GP) is a powerful technique for Symbolic Regression. However, because it is a new area, many improvements can be obtained changing the basic behavior of the method. In this way, this work develop a different Genetic Programming algorithm doing some modifications on the classical GP algorithm and adding some concepts of Evolution Strategies. The new approach was evaluated using two instances of Symbolic Regression problem -- the Binomial--3 problem (a tunably difficult problem), proposed in [4] and the problem of Modelling Software Reliability Growth (an application of Symbolic Regression). The discovered results were compared with the classical GP algorithm. The Symbolic Regression problems obtained excellent results and an improvement was detected using the proposed approach.