- author = "Perla Juarez-Smith and Leonardo Trujillo",
- title = "Integrating Local Search within {neat-GP}",
- booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation",
- year = "2016",
- editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing",
- isbn13 = "978-1-4503-4323-7",
- pages = "993--996",
- address = "Denver, Colorado, USA",
- month = "20-24 " # jul,
- keywords = "genetic algorithms, genetic programming",
- organisation = "SIGEVO",
-
DOI = "
doi:10.1145/2908961.2931659",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- abstract = "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, it uses inefficient search operators that operate at the syntax level. The first problem has been the subject of a fair amount of research over the years. Regarding the second problem, one approach is to use alternative search operators, for instance geometric semantic operators. However, another approach is to introduce greedy local search strategies, combining the syntactic search performed by standard GP with local search strategies for solution tuning, which is a simple strategy that has comparatively received much less attention. This work combines a recently proposed bloat-free GP called neat-GP with a local search strategy. One benefit of using a bloat-free GP is that it reduces the size of the parameter space confronted by the local searcher, offsetting some of the added computational cost. The algorithm is validated on a real-world problem with promising results.",
-
notes = "GECCO Student Workshop, Best Paper Award 2nd
Place.
Distributed at GECCO-2016.",
