Abstract
A new Genetic Programming variant called Liquid State Genetic Programming (LSGP) is proposed in this paper. LSGP is a hybrid method combining a dynamic memory for storing the inputs (the liquid) and a Genetic Programming technique used for the problem solving part. Several numerical experiments with LSGP are performed by using several benchmarking problems. Numerical experiments show that LSGP performs similarly and sometimes even better than standard Genetic Programming for the considered test problems.
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References
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Subprograms. MIT Press, Cambridge (1994)
Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14, 2531–2560 (2002)
Miller, J.F., Job, D., Vassilev, V.K.: Principles in the Evolutionary Design of Digital Circuits - Part I. Genetic Programming and Evolvable Machines 1(1-2), 7–35 (2000)
Natschläger, T., Maass, W., Markram, H.: The ”liquid computer”: A novel strategy for real-time computing on time series. Special Issue on Foundations of Information Processing of TELEMATIK 8, 39–43 (2002)
Poli, R., Langdon, W.B.: Sub-machine Code Genetic Programming. In: Spector, L., Langdon, W.B., O’Reilly, U.-M., Angeline, P.J. (eds.) Advances in Genetic Programming 3, MIT Press, Cambridge (1999)
Poli, R., Page, J.: Solving High-Order Boolean Parity Problems with Smooth Uniform Crossover, Sub-Machine Code GP and Demes. Journal of Genetic Programming and Evolvable Machines, Kluwer, 1–21 (2000)
Prechelt, L.: PROBEN1: A Set of Neural Network Problems and Benchmarking Rules, Technical Report 21, University of Karlsruhe (1994), available from ftp://ftp.cs.cmu.edu/afs/cs/project/connect/bench/contrib/prechelt/proben1.tar.gz
UCI Machine Learning Repository, available from http://www.ics.uci.edu/~mlearn/MLRepository.html
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Oltean, M. (2007). Liquid State Genetic Programming. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71618-1_25
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DOI: https://doi.org/10.1007/978-3-540-71618-1_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71589-4
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