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

Published: 12 July 2014 Publication History

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 behavior 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.

References

[1]
A. Bajurnow and V. Ciesielski. Layered learning for evolving goal scoring behavior in soccer players. In Proceedings of the 2004 IEEE Congress on Evolutionary Computation, pages 1828--1835, Portland, Oregon, 20--23 June 2004. IEEE Press.
[2]
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation, IEEE Transactions on, 6(2):182 --197, apr 2002.
[3]
M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: An update. SIGKDD Explor. Newsl., 11(1):10--18, Nov. 2009.
[4]
T. Haynes. On-line adaptation of search via knowledge reuse. In J. R. Koza, K. Deb, M. Dorigo, D. B. Fogel, M. Garzon, H. Iba, and R. L. Riolo, editors, Genetic Programming 1997: Proceedings of the Second Annual Conference, pages 156--161, Stanford University, CA, USA, 13--16 July 1997. Morgan Kaufmann.
[5]
M. Hollander and D. Wolfe. Nonparametric Statistical Methods. A Wiley-Interscience publication. Wiley, 1999.
[6]
G. S. Hornby and J. B. Pollack. Creating high-level components with a generative representation for body-brain evolution. Artif. Life, 8(3):223--246, 2002.
[7]
G. Kanji. 100 Statistical Tests. SAGE Publications, 1999.
[8]
J. D. Knowles, R. A. Watson, and D. Corne. Reducing local optima in single-objective problems by multi-objectivization. In EMO '01: Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization, pages 269--283, London, UK, 2001. Springer-Verlag.
[9]
K. Krawiec and J. Swan. Pattern-guided genetic programming. In C. Blum, et al., editors, GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference, pages 949--956, Amsterdam, The Netherlands, 6--10 July 2013. ACM.
[10]
J. Lehman and K. O. Stanley. Abandoning objectives: Evolution through the search for novelty alone. Evolutionary Computation, 19(2):189--223, Summer 2011.
[11]
S. Luke. ECJ evolutionary computation system, 2002. (http://cs.gmu.edu/eclab/projects/ecj/).
[12]
J. McDermott, D. R. White, S. Luke, L. Manzoni, M. Castelli, L. Vanneschi, W. Jaskowski, K. Krawiec, R. Harper, K. De Jong, and U.-M. O'Reilly. Genetic programming needs better benchmarks. In T. Soule, et al., editors, GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, pages 791--798, Philadelphia, Pennsylvania, USA, 7--11 July 2012. ACM.
[13]
R. I. B. McKay. Fitness sharing in genetic programming. In D. Whitley, D. Goldberg, E. Cantu-Paz, L. Spector, I. Parmee, and H.-G. Beyer, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), pages 435--442, Las Vegas, Nevada, USA, 10--12 July 2000. Morgan Kaufmann.
[14]
N. F. McPhee, B. Ohs, and T. Hutchison. Semantic building blocks in genetic programming. In M. O'Neill, L. Vanneschi, S. Gustafson, A. I. Esparcia Alcazar, I. De Falco, A. Della Cioppa, and E. Tarantino, editors, Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008, volume 4971 of Lecture Notes in Computer Science, pages 134--145, Naples, 26--28 Mar. 2008. Springer.
[15]
A. Moraglio, K. Krawiec, and C. G. Johnson. Geometric semantic genetic programming. In C. A. Coello Coello, V. Cutello, K. Deb, S. Forrest, G. Nicosia, and M. Pavone, editors, Parallel Problem Solving from Nature, PPSN XII (part 1), volume 7491 of Lecture Notes in Computer Science, pages 21--31, Taormina, Italy, Sept. 1--5 2012. Springer.
[16]
A. Moraglio and A. Mambrini. Runtime analysis of mutation-based geometric semantic genetic programming for basis functions regression. In C. Blum, et al., editors, GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference, pages 989--996, Amsterdam, The Netherlands, 6--10 July 2013. ACM.
[17]
J. P. Rosca and D. H. Ballard. Discovery of subroutines in genetic programming. In P. J. Angeline and K. E. Kinnear, Jr., editors, Advances in Genetic Programming 2, chapter 9, pages 177--201. MIT Press, Cambridge, MA, USA, 1996.
[18]
C. Ryan, M. Keijzer, and M. Cattolico. Favorable biasing of function sets using run transferable libraries. In U.-M. O'Reilly, T. Yu, R. L. Riolo, and B. Worzel, editors, Genetic Programming Theory and Practice II, chapter 7, pages 103--120. Springer, Ann Arbor, 13--15 May 2004.
[19]
S. Singh, R. L. Lewis, A. G. Barto, and J. Sorg. Intrinsically motivated reinforcement learning: An evolutionary perspective. IEEE Trans. on Auton. Ment. Dev., 2(2):70--82, June 2010.
[20]
L. Spector, D. M. Clark, I. Lindsay, B. Barr, and J. Klein. Genetic programming for finite algebras. In M. Keijzer, et al., editors, GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation, pages 1291--1298, Atlanta, GA, USA, 12--16 July 2008. ACM.

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    cover image ACM Conferences
    GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation
    July 2014
    1478 pages
    ISBN:9781450326629
    DOI:10.1145/2576768
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 12 July 2014

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    Author Tags

    1. archive
    2. behavioral evaluation
    3. genetic programming
    4. multiobjective evolutionary computation
    5. program semantics
    6. program synthesis
    7. search operators

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    GECCO '14
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    GECCO '14: Genetic and Evolutionary Computation Conference
    July 12 - 16, 2014
    BC, Vancouver, Canada

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    GECCO '14 Paper Acceptance Rate 180 of 544 submissions, 33%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    • (2024)Predictive Genetic Programming Approaches for Swell-Shrink Soil CompactionEarth Science Informatics10.1007/s12145-024-01482-517:6(5967-5990)Online publication date: 19-Sep-2024
    • (2023)Lexicase SelectionProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3595035(976-989)Online publication date: 15-Jul-2023
    • (2023)Down-Sampled Epsilon-Lexicase Selection for Real-World Symbolic Regression ProblemsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590400(1109-1117)Online publication date: 15-Jul-2023
    • (2022)Lexicase selectionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533633(1385-1397)Online publication date: 9-Jul-2022
    • (2022)Semantic schema based genetic programming for symbolic regressionApplied Soft Computing10.1016/j.asoc.2022.108825122:COnline publication date: 1-Jun-2022
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    • (2021)Lexicase SelectionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3449726.3461408(839-855)Online publication date: 7-Jul-2021
    • (2021)A semantic genetic programming framework based on dynamic targetsGenetic Programming and Evolvable Machines10.1007/s10710-021-09419-3Online publication date: 5-Oct-2021
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