Jan Žegklitz
Petr Pošík
ABSTRACT
Grammatical Evolution is an algorithm of Genetic Programming but it is capable of evolving programs in an arbitrary language given by a user-provided context-free grammar. We present a way how to apply Multi-Gene idea, known from Multi-Gene Genetic Programming, to Grammatical Evolution, just by modifying the given grammar. We also describe modifications which improve the behavior of such algorithm, called Multi-Gene Grammatical Evolution. We compare the resulting system to GPTIPS, an existing implementation of MGGP.
- K. Bache and M. Lichman. UCI machine learning repository, 2013. http://archive.ics.uci.edu/ml.Google Scholar
- P. Cortez and A. d. J. R. Morais. A data mining approach to predict forest fires using meteorological data. 2007.Google Scholar
- J. H. Halton. On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals. Numerische Mathematik, 2(1):84--90, 1960. Google ScholarDigital Library
- R. Harper and A. Blair. A structure preserving crossover in grammatical evolution. In 2005 IEEE Congress on Evolutionary Computation, volume 3, pages 2537--2544, 2005.Google ScholarCross Ref
- M. Hinchliffe, H. Hiden, B. McKay, M. Willis, M. Tham, and G. Barton. Modelling chemical process systems using a multi-gene genetic programming algorithm. In Late Breaking Paper, GP'96, pages 56--65, Stanford, USA, 1996.Google Scholar
- J. R. Koza. Genetic programming: on the programming of computers by means of natural selection, volume 1. MIT press, 1992. Google ScholarDigital Library
- J. R. Koza. Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, 1994. Google ScholarDigital Library
- D. J. Montana. Strongly typed genetic programming. Evolutionary computation, 3(2):199--230, 1995. Google ScholarDigital Library
- C. Ryan, J. Collins, and M. O'Neill. Grammatical evolution: Evolving programs for an arbitrary language. In Genetic Programming, volume 1391 of Lecture Notes in Computer Science, pages 83--96. Springer Berlin Heidelberg, 1998. Google ScholarDigital Library
- D. P. Searson. GPTIPS2: an open-source software platform for symbolic datamining. In A. H. Gandomi, A. H. Alavi, and C. Ryan, editors, Springer Handbook of Genetic Programming Applications. 2015. In press.Google ScholarCross Ref
- D. P. Searson, D. E. Leahy, and M. J. Willis. GPTIPS: an open source genetic programming toolbox for multigene symbolic regression. In Proceedings of the International MultiConference of Engineers and Computer Scientists, volume 1, pages 77--80, March 2010.Google Scholar
Index Terms
- Symbolic Regression by Grammar-based Multi-Gene Genetic Programming
Recommendations
Attribute grammar genetic programming algorithm for automatic code parallelization
ICHIT'11: Proceedings of the 5th international conference on Convergence and hybrid information technologyA method is presented for evolving individuals that use an Attribute Grammar (AG) in a generative way. AGs are considerably more flexible and powerful than the closed, context free grammars normally employed by GP. Rather than evolving derivation trees ...
A Comparison of three evolutionary strategies for multiobjective genetic programming
We report what we believe to be the first comparative study of multi-objective genetic programming (GP) algorithms on benchmark symbolic regression and machine learning problems. We compare the Strength Pareto Evolutionary Algorithm (SPEA2), the Non-...
A comparison of grammatical genetic programming grammars for controlling femtocell network coverage
We study grammars used in grammatical genetic programming (GP) which create algorithms that control the base station pilot power in a femtocell network. The overall goal of evolving algorithms for femtocells is to create a continuous online evolution of ...
Comments