Liquid State Genetic Programming
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.
KeywordsGenetic Programming Function Symbol Dynamic Memory Actual Problem Solver Symbolic Regression
Unable to display preview. Download preview PDF.
- 2.Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Subprograms. MIT Press, Cambridge (1994)Google Scholar
- 5.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)Google Scholar
- 6.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)Google Scholar
- 7.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)Google Scholar
- 8.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
- 9.UCI Machine Learning Repository, available from http://www.ics.uci.edu/~mlearn/MLRepository.html