PlumX Metrics
Embed PlumX Metrics

Reducing Bloat in GP with Multiple Objectives

Natural Computing Series, ISSN: 1619-7127, Page: 177-200
2008
  • 15
    Citations
  • 0
    Usage
  • 37
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    15
    • Citation Indexes
      15
  • Captures
    37

Book Chapter Description

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 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.

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know