Random tree generation for genetic programming
This paper introduces a random tree generation algorithm for GP (Genetic Programming). Generating random trees is an essential part of GP. However, the recursive method commonly used in GP does not necessarily generate random trees, i.e the standard GP initialization procedure does not sample the space of possible initial trees uniformly. This paper proposes a truly random tree generation procedure for GP. Our approach is grounded upon a bijection method, i.e., a 1–1 correspondence between a tree with n nodes and some simple word composed by letters x and y. We show how to use this correspondence to generate a GP tree and how GP search is improved by using this “randomness”.
Unable to display preview. Download preview PDF.
- [Alonso95]Alonso,L. and Schott,R., Random Generation of Trees, Kluwer Academic Publishers, 1995Google Scholar
- [Iba95]Iba,H., Generating Random Trees for Genetic Programming, Computers ETL-TR-95-35, 1995Google Scholar
- [Koza92]Koza, J. Genetic Programming, On the Programming of Computers by means of Natural Selection, MIT Press, 1992Google Scholar
- [Lang95]Lang,K.J., Hill Climbing Beats Genetic Search on a Boolean Circuit Synthesis Problem of Koza's, in Proc. of 12th Machine Learning Workshop, pp.340–344, 1995Google Scholar
- [Lovasz79]Lovasz, L. Combinational problem and exercises, Akademiai Kiado, Budapest, 1979Google Scholar
- [Montana95]Montana, D.J., Strongly Typed Genetic Programming, in Evolutionary Computation, vol.3, no.2, pp.199–230, 1995Google Scholar
- [Oakley94]Oakley, H. Two Scientific Applications of Genetic Programming: Stack Filters and Non-Linear Equation Fitting to Chaotic Data, in Advances in Genetic Programming, (ed. Kenneth E. Kinnear, Jr.), MIT Press, 1994Google Scholar