Behavioral programming: a broader and more detailed take on semantic GP

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Krawiec:2014:GECCO,

• author = "Krzysztof Krawiec and Una-May O'Reilly",
• title = "Behavioral programming: a broader and more detailed take on semantic GP",
• booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation",
• year = "2014",
• editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges",
• isbn13 = "978-1-4503-2662-9",
• pages = "935--942",
• keywords = "genetic algorithms, genetic programming",
• month = "12-16 " # jul,
• organisation = "SIGEVO",
• address = "Vancouver, BC, Canada",
• note = "Best paper",
• URL = "http://doi.acm.org/10.1145/2576768.2598288",
• DOI = "doi:10.1145/2576768.2598288",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "In evolutionary computation, the fitness of a candidate solution conveys sparse feedback. Yet in many cases, candidate solutions can potentially yield more information. In genetic programming (GP), one can easily examine program behaviour on particular fitness cases or at intermediate execution states. However, how to exploit it to effectively guide the search remains unclear. In this study we apply machine learning algorithms to features describing the intermediate behavior of the executed program. We then drive the standard evolutionary search with additional objectives reflecting this intermediate behavior. The machine learning functions independent of task-specific knowledge and discovers potentially useful components of solutions (subprograms), which we preserve in an archive and use as building blocks when composing new candidate solutions. In an experimental assessment on a suite of benchmarks, the proposed approach proves more capable of finding optimal and/or well-performing solutions than control methods.",
• notes = "Also known as \cite{2598288} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)",

}

Genetic Programming entries for Krzysztof Krawiec Una-May O'Reilly

Citations