| Abstract: |
This chapter investigates the use of multiobjective techniques in Genetic Programming (GP) in order to evolve compact programs and to reduce the effects caused by bloating. The underlying
approach considers the program size as a second, independent objective besides the program functionality and several studies have found this concept to be successful in reducing bloat. Based on
one specific algorithm, we demonstrate the principle of multiobjective GP and show how to apply Pareto-based strategies to GP. This approach outperforms four classical strategies to reduce bloat
with regard to both convergence speed and size of the produced programs on an even-parity problem. Additionally, we investigate the question of why the Pareto-based strategies can be more
effective in reducing bloat than alternative strategies on several test problems. The analysis falsifies the hypothesis that the small but less functional individuals that are kept in the
population act as building-blocks for larger correct solutions. This leads to the conclusion that the advantages are probably due to the increased diversity in the population. |
