Section: New Results

Use of local Search in Genetic Programming

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 in many works, with many bloat control proposals. Regarding the second problem, one approach is to use alternative search operators, for instance geometric semantic operators, to improve convergence. In [36], 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 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.

Authors: Leonardo Trujillo, Emigdio Z-Flores, Perla S. Juarez Smith, Pierrick Legrand (Inria CQFD), Sara Silva, Mauro Castelli, Leonardo Vanneschi, Oliver Schutze and Luis Munoz.