A (\mu + \lambda) - GP Algorithm and its use for Regression Problems
Eduardo Costa, Aurora Pozo · Proceedings - International Conference on Tools with Artificial Intelligence, TAI · 2006
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 (J.M. Daida et al., 2001) 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