Alan Piszcz
ABSTRACT
Recently there has been considerable interest in determining whether, and how much, evolutionary pressure for genetic robustness influences evolutionary processes. In this paper, we attempt to show that this evolutionary pressure does have a significant effect in typical genetic programming problems. Specifically we demonstrate that in a standard genetic programming implementation to solve a symbolic regression problem, pressure for genetic robustness forces the population away from high fitness, but less robust, solutions in favor of solutions with lower fitness, but higher genetic robustness.
- F. A. Castillo, K. A. Marshall, J. L. Green, and A. K. Kordon. Symbolic regression in design of experiments: A case study with linearizing transformations. In W. B. Langdon, E. Cantú-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A. C. Schultz, J. F. Miller, E. Burke, and N. Jonoska, editors, GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 1043--1047, New York, 9-13 July 2002. Morgan Kaufmann Publishers Google Scholar
Digital Library
- J. W. Davidson, D. A. Savic, and G. A. Walters. Symbolic and numerical regression: Experiments and applications. Information Sciences, 150(1-2):95--117, 2003. Google ScholarDigital Library
- K. DeJong. An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, 1975.Google Scholar
- J. Duffy and J. Engle-Warnick. Using symbolic regression to infer strategies from experimental data. In D. A. Belsley and C. F. Baum, editors, Fifth International Conference: Computing in Economics and Finance, page 150, Boston College, MA, USA, 24-26 June 1999. Book of Abstracts.Google Scholar
- J. A. Edlund and C. Adami. Evolution of robustness in digital organisms. Artif. Life, 10(2):167--179, 2004. Google ScholarDigital Library
- J. Eggermont. Data Mining using Genetic Programming: Classification and Symbolic Regression. PhD thesis, Leiden University, The Netherlands, 2005.Google Scholar
- J. Arjan G. M. de Visser, J. Hermisson, Gunter P. Wagner, Lauren Ancel Meyers, Homayoun Bagheri-Chaichian, Jeffrey L. Blanchard, Lin Chao, James M. Cheverud, Santiago F. Elena, Walter Fontana, Greg Gibson, Thomas F. Hansen, David Krakauer, Richard C. Lewontin, Charles Ofria, Sean H. Rice, George von Dassow, Andreas Wagner, and Michael C. Whitlock. Perspective: Evolution and detection of genetic robustness. Evolution, 57:1959--1972, 2003.Google Scholar
- A. Kordon, F. Castillo, G. Smits, and M. Kotanchek. Application issues of genetic programming in industry. In T. Yu, R. L. Riolo, and B. Worzel, editors, Genetic Programming Theory and Practice III. Kluwer, Ann Arbor, 12-14 May 2005.Google Scholar
- A. Kordon and C.-T. Lue. Symbolic regression modeling of blown film process effects. In Proceedings of the 2004 IEEE Congress on Evolutionary Computation, pages 561--568, Portland, Oregon, 20-23 June 2004. IEEE Press.Google ScholarCross Ref
- J. R. Koza. A genetic approach to econometric modeling. In Sixth World Congress of the Econometric Society, Barcelona, Spain, 1990.Google Scholar
- D. C. Krakauer and J. B. Plotkin. Redundancy, antiredundancy, and the robustness of genomes. Proceedings of the National Academy of Sciences, 99(3):1405--1409, 2002.Google ScholarCross Ref
- P. Nordin and W. Banzhaf. Genetic reasoning evolving proofs with genetic search. Technical report, University Dortmund, 1996.Google Scholar
- G. R. Raidl. A Hybrid GP Approach for Numerically Robust Symbolic Regression, 1998.Google Scholar
- M. L. Siegal and A. Bergman. Waddingtons canalization revisited: Developmental stability and evolution. Proceedings of the National Academy of Sciences, 99(16):10528--10532, 2002.Google ScholarCross Ref
- T. Soule. Resilient individuals improve evolutionary search. Artificial Life, 12(1), 2005. Google ScholarDigital Library
- M. J. Streeter. The root causes of code growth in genetic programming. In C. Ryan, T. Soule, M. Keijzer, E. Tsang, R. Poli, and E. Costa, editors, Genetic Programming, Proceedings of EuroGP'2003, volume 2610 of LNCS, pages 443--454, Essex, April 2003. Springer-Verlag. Google ScholarDigital Library
- C. H. Waddington. Canalization of development and the inheritance of acquired characters. Nature, 150:563--565, 1942.Google ScholarCross Ref
- C. Wilke and C. Adami. Evolution of mutational robustness. Mutation Research, 522:3--11, 2002.Google ScholarCross Ref
Recommendations
Evolutionary dynamics on multiple scales: a quantitative analysis of the interplay between genotype, phenotype, and fitness in linear genetic programming
Redundancy is a ubiquitous feature of genetic programming (GP), with many-to-one mappings commonly observed between genotype and phenotype, and between phenotype and fitness. If a representation is redundant, then neutral mutations are possible. A ...
Robustness of populations in stochastic environments
GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary ComputationWe consider stochastic versions of OneMax and LeadingOnes and analyze the performance of evolutionary algorithms with and without populations on these problems. It is known that the (1+1) EA on OneMax performs well in the presence of very small noise, ...
A multipopulation genetic algorithm aimed at multimodal optimization
This paper considers a new method that enables a genetic algorithm (GA) to identify and maintain multiple optima of a multimodal function, by creating subpopulations within the niches defined by the multiple optima, thus warranting a good 'diversity'. ...
Comments