Local Search is Underused in Genetic Programming

Leonardo Trujillo, Emigdio Z-Flores, Perla S. Juárez-Smith, Pierrick Legrand, Sara Silva, Mauro Castelli, Leonardo Vanneschi, Oliver Schütze, Luis Muñoz

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


There are two important limitations of standard tree-based genetic programming (GP). First, GP tends to evolve unnecessarily large programs, what is referred to as bloat. Second, GP uses inefficient search operators that focus on modifying program syntax. The first problem has been studied extensively, with many works proposing bloat control methods. Regarding the second problem, one approach is to use alternative search operators, for instance geometric semantic operators, to improve convergence. In this work, our goal is to experimentally show that both problems can be effectively addressed by incorporating a local search optimizer as an additional search operator. Using real-world problems, we show that this rather simple strategy can improve the convergence and performance of tree-based GP, while also reducing program size. Given these results, a question arises: Why are local search strategies so uncommon in GP? A small survey of popular GP libraries suggests to us that local search is underused in GP systems. We conclude by outlining plausible answers for this question and highlighting future work.
Original languageEnglish
Title of host publicationGenetic Programming Theory and Practice XIV
EditorsRick Riolo, Bill Worzel, Brian Goldman, Bill Tozier
ISBN (Electronic)978-3-319-97088-2
ISBN (Print)978-3-319-97087-5
Publication statusPublished - 25 Oct 2018

Publication series

NameGenetic and Evolutionary Computation
ISSN (Print)1932-0167


  • Genetic programming (GP)
  • Evolvability
  • Local search (optimization)
  • Symbolic regression
  • Numerical optimization
  • Bloat
  • NeuroEvolution of augmenting topologies


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