A Continuous Approach to Genetic Programming
Differential Evolution (DE) is an evolutionary heuristic for continuous optimization problems. In DE, solutions are coded as vectors of floats that evolve by crossover with a combination of best and random individuals from the current generation. Experiments to apply DE to automatic programming were made recently by Veenhuis, coding full program trees as vectors of floats (Tree Based Differential Evolution or TreeDE). In this paper, we use DE to evolve linear sequences of imperative instructions, which we call Linear Differential Evolutionary Programming (LDEP). Unlike TreeDE, our heuristic provides constant management for regression problems and lessens the tree-depth constraint on the architecture of solutions. Comparisons with TreeDE and GP show that LDEP is appropriate to automatic programming.
KeywordsRegression Problem Crossover Rate Continuous Approach Destination Register Symbolic Regression
Unable to display preview. Download preview PDF.
- 2.Langdon, W.B., Poli, R.: Why ants are hard. Tech. Rep. CSRP-98-4, University of Birmingham, School of Computer Science (January 1998); presented at GP 1998Google Scholar
- 4.Luke, S., Panait, L.: Is the perfect the enemy of the good? In: Langdon, W.B., Cantú-Paz, E., Mathias, K., Roy, R., Davis, D., Poli, R., Balakrishnan, K., Honavar, V., Rudolph, G., Wegener, J., Bull, L., Potter, M.A., Schultz, A.C., Miller, J.F., Burke, E., Jonoska, N. (eds.) GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, July 9-13, pp. 820–828. Morgan Kaufmann Publishers, New York (2002)Google Scholar
- 5.O’Neill, M., Brabazon, A.: Grammatical differential evolution. In: International Conference on Artificial Intelligence (ICAI 2006), Las Vegas, Nevada, USA, pp. 231–236 (2006)Google Scholar
- 6.Price, K.: Differential evolution: a fast and simple numerical optimizer. In: Biennial Conference of the North American Fuzzy Information Processing Society, pp. 524–527 (1996)Google Scholar
- 9.Veenhuis, C.B.: Tree based differential evolution. In: , pp. 208–219 (2009)Google Scholar