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Compositional genetic programming for symbolic regression

Published: 19 July 2022 Publication History

Abstract

In genetic programming, candidate solutions are compositional structures that can be easily decomposed into constituent parts and assembled from them. This property is extensively used in search operators, but rarely exploited in other stages of evolutionary search. We propose an approach to symbolic regression that augments the search state by maintaining, apart from the population of candidate solutions, a library of subprograms and a library of program contexts, i.e. partial programs that need to be supplemented by a subprogram to form a complete program. This allows us to identify the promising program components and guide search using two mechanisms in parallel: the conventional fitness-based selection pressure, and matching contexts with subprograms using a gradient-based mechanism. In experimental assessment, the approach significantly outperforms the control setups and the conventional GP. Maintaining subprograms and contexts in efficient data structures prevents redundancy and lessens the demand for computational resources, in particular memory.

References

[1]
Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau, and Christian Gagné. 2012. DEAP: Evolutionary Algorithms Made Easy. Journal of Machine Learning Research 2171--2175, 13 (jul 2012).
[2]
Mario Graff, Ariel Graff-Guerrero, and Jaime Cerda-Jacobo. 2014. Semantic Crossover based on the Partial Derivative Error. In 17th European Conference on Genetic Programming (LNCS, Vol. 8599), Miguel Nicolau, Krzysztof Krawiec, Malcolm I. Heywood, Mauro Castelli, Pablo Garcia-Sanchez, Juan J. Merelo, Victor M. Rivas Santos, and Kevin Sim (Eds.). Springer, Granada, Spain, 37--47.
[3]
Myles Hollander, Douglas A Wolfe, and Eric Chicken. 2013. Nonparametric statistical methods. Vol. 751. John Wiley & Sons.
[4]
John R. Koza. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press.
[5]
John R. Koza. 1994. Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press.
[6]
Krzysztof Krawiec and Tomasz Pawlak. 2013. Approximating geometric crossover by semantic backpropagation. In GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference, Christian Blum, Enrique Alba, Anne Auger, Jaume Bacardit, Josh Bongard, Juergen Branke, Nicolas Bredeche, Dimo Brockhoff, Francisco Chicano, Alan Dorin, Rene Doursat, Aniko Ekart, Tobias Friedrich, Mario Giacobini, Mark Harman, Hitoshi Iba, Christian Igel, Thomas Jansen, Tim Kovacs, Taras Kowaliw, Manuel Lopez-Ibanez, Jose A. Lozano, Gabriel Luque, John McCall, Alberto Moraglio, Alison Motsinger-Reif, Frank Neumann, Gabriela Ochoa, Gustavo Olague, Yew-Soon Ong, Michael E. Palmer, Gisele Lobo Pappa, Konstantinos E. Parsopoulos, Thomas Schmickl, Stephen L. Smith, Christine Solnon, Thomas Stuetzle, El-Ghazali Talbi, Daniel Tauritz, and Leonardo Vanneschi (Eds.). ACM, Amsterdam, The Netherlands, 941--948.
[7]
Krzysztof Krawiec and Tomasz Pawlak. 2013. Locally geometric semantic crossover: a study on the roles of semantics and homology in recombination operators. Genetic Programming and Evolvable Machines 14, 1 (March 2013), 31--63.
[8]
James McDermott, David R. White, Sean Luke, Luca Manzoni, Mauro Castelli, Leonardo Vanneschi, Wojciech Jaskowski, Krzysztof Krawiec, Robin Harper, Kenneth De Jong, and Una-May O'Reilly. 2012. Genetic programming needs better benchmarks. In GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, Terry Soule, Anne Auger, Jason Moore, David Pelta, Christine Solnon, Mike Preuss, Alan Dorin, Yew-Soon Ong, Christian Blum, Dario Landa Silva, Frank Neumann, Tina Yu, Aniko Ekart, Will Browne, Tim Kovacs, Man-Leung Wong, Clara Pizzuti, Jon Rowe, Tobias Friedrich, Giovanni Squillero, Nicolas Bredeche, Stephen L. Smith, Alison Motsinger-Reif, Jose Lozano, Martin Pelikan, Silja Meyer-Nienberg, Christian Igel, Greg Hornby, Rene Doursat, Steve Gustafson, Gustavo Olague, Shin Yoo, John Clark, Gabriela Ochoa, Gisele Pappa, Fernando Lobo, Daniel Tauritz, Jurgen Branke, and Kalyanmoy Deb (Eds.). ACM, Philadelphia, Pennsylvania, USA, 791--798.
[9]
Q. U. Nguyen, X. H. Nguyen, M. O'Neill, R. I. Mckay, and Edgar Galván-López. 2011. Semantically-Based Crossover in Genetic Programming: Application to Real-valued Symbolic Regression. GPEM 12 (2011), 91--119. Issue 2.
[10]
Alexander Topchy and William F. Punch. 2001. Faster Genetic Programming based on Local Gradient Search of Numeric Leaf Values. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), Lee Spector, Erik D. Goodman, Annie Wu, W. B. Langdon, Hans-Michael Voigt, Mitsuo Gen, Sandip Sen, Marco Dorigo, Shahram Pezeshk, Max H. Garzon, and Edmund Burke (Eds.). Morgan Kaufmann, San Francisco, California, USA, 155--162. http://garage.cse.msu.edu/papers/GARAGe01-07-01.pdf
[11]
Marco Virgolin, Tanja Alderliesten, Cees Witteveen, and Peter A. N. Bosman. 2017. Scalable Genetic Programming by Gene-pool Optimal Mixing and Input-space Entropy-based Building-block Learning. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '17). ACM, Berlin, Germany, 1041--1048.

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cover image ACM Conferences
GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2022
2395 pages
ISBN:9781450392686
DOI:10.1145/3520304
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 19 July 2022

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  1. genetic programming
  2. modularity
  3. semantic genetic programming
  4. symbolic regression

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