Measurements for understanding the behavior of the genetic algorithm in dynamic environments: a case study using the Shaky Ladder Hyperplane-Defined Functions

We describe a set of measures to examine the behavior of the Genetic Algorithm (GA) in dynamic environments. We describe how to use both average and best measures to look at performance, satisficability, robustness, and diversity. We use these measures to examine GA behavior with a recently devised dynamic test suite, the Shaky Ladder Hyperplane-Defined Functions (sl-hdf's). This test suite can generate random problems with similar levels of difficulty and provides a platform allowing systematic controlled observations of the GA in dynamic environments. We examine the results of these measures in two different versions of the sl-hdf's, one static and one regularly-changing. We provide explanations for the observations in these two different environments, and give suggestions as to future work.

[1]  Edward O. Wilson,et al.  The Current State of Biological Diversity , 1988 .

[2]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[3]  R.W. Morrison,et al.  A test problem generator for non-stationary environments , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[4]  L. Darrell Whitley,et al.  Evaluating Evolutionary Algorithms , 1996, Artif. Intell..

[5]  John R. Koza,et al.  Genetic programming (videotape): the movie , 1992 .

[6]  Helen G. Cobb,et al.  An Investigation into the Use of Hypermutation as an Adaptive Operator in Genetic Algorithms Having Continuous, Time-Dependent Nonstationary Environments , 1990 .

[7]  Ernesto Benini,et al.  Genetic Diversity as an Objective in Multi-Objective Evolutionary Algorithms , 2003, Evolutionary Computation.

[8]  William Rand,et al.  Shaky Ladders, Hyperplane-Defined Functions and Genetic Algorithms: Systematic Controlled Observation in Dynamic Environments , 2005, EvoWorkshops.

[9]  A. Sima Etaner-Uyar,et al.  Towards an analysis of dynamic environments , 2005, GECCO '05.

[10]  William Rand,et al.  The problem with a self-adaptative mutation rate in some environments: a case study using the shaky ladder hyperplane-defined functions , 2005, GECCO '05.

[11]  John J. Grefenstette,et al.  Genetic Algorithms for Changing Environments , 1992, PPSN.

[12]  Jason M. Daida,et al.  Optimal Mutation and Crossover Rates for a Genetic Algorithm Operating in a Dynamic Environment , 1998, Evolutionary Programming.

[13]  Jürgen Branke,et al.  Memory enhanced evolutionary algorithms for changing optimization problems , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[14]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[15]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[16]  H. Simon,et al.  Models of Man. , 1957 .

[17]  John H. Holland,et al.  Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions , 2000, Evolutionary Computation.

[18]  Erica Jen,et al.  Stable or robust? What's the difference? , 2003, Complex..

[19]  Jürgen Branke,et al.  Evolutionary Optimization in Dynamic Environments , 2001, Genetic Algorithms and Evolutionary Computation.

[20]  Vidroha Debroy,et al.  Genetic Programming , 1998, Lecture Notes in Computer Science.