An Evolutionary Approach for Time Dependent Optimization

Many real-world problems involve measures of objectives that may be dynamically optimized. The application of evolutionary algorithms, such as genetic algorithms, in time dependent optimization is currently receiving growing interest as potential applications are numerous ranging from mobile robotics to real time process command. Moreover, constant evaluation functions skew results relative to natural evolution so that it has become a promising gap to combine effectiveness and diversity in a genetic algorithm. This paper features both theoretical and empirical analysis of the behavior of genetic algorithms in such an environment. We present a comparison between the effectivenss of traditional genetic algorithm and the dual genetic algorithm which has revealed to be a particularly adaptive tool for optimizing a lot of diversified classes of functions. This comparison has been performed on a model of dynamical environments which characteristics are analyzed in order to establish the basis of a testbed for further experiments. We also discuss fundamental properties that explain the effectiveness of the dual paradigm to manage dynamical environments.

[1]  Michael D. Vose,et al.  Generalizing the Notion of Schema in Genetic Algorithms , 1991, Artif. Intell..

[2]  Terence C. Fogarty,et al.  A Comparative Study of Steady State and Generational Genetic Algorithms , 1996, Evolutionary Computing, AISB Workshop.

[3]  Philippe Collard,et al.  Genetic operators in a dual genetic algorithm , 1995, Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence.

[4]  Philippe Collard,et al.  Genetic Algorithms and Relational Landscapes , 1996, PPSN.

[5]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[7]  Annie S. Wu,et al.  A Survey of Intron Research in Genetics , 1996, PPSN.

[8]  Nicholas J. Radcliffe,et al.  Forma Analysis and Random Respectful Recombination , 1991, ICGA.

[9]  David E. Goldberg,et al.  Diploidy and Dominance in Artificial Genetic Search , 1992, Complex Syst..

[10]  Melanie Mitchell,et al.  Genetic Algorithms and Artiicial Life , 1993 .

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

[12]  Lashon B. Booker,et al.  Proceedings of the fourth international conference on Genetic algorithms , 1991 .

[13]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[14]  John J. Grefenstette,et al.  Genetic Algorithms for Tracking Changing Environments , 1993, ICGA.

[15]  L. Darrell Whitley,et al.  An Executable Model of a Simple Genetic Algorithm , 1992, FOGA.

[16]  James R. Levenick,et al.  Metabits: Generic Endogenous Crossover Control , 1995, International Conference on Genetic Algorithms.

[17]  Michael D. Vose,et al.  Modeling Simple Genetic Algorithms , 1995, Evolutionary Computation.

[18]  Arantxa Etxeverria.  The Origins of Order , 1993 .

[19]  William M. Spears,et al.  Crossover or Mutation? , 1992, FOGA.

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

[21]  Kathleen M. Swigger,et al.  An Analysis of Genetic-Based Pattern Tracking and Cognitive-Based Component Tracking Models of Adaptation , 1983, AAAI.

[22]  Gunar E. Liepins,et al.  Punctuated Equilibria in Genetic Search , 1991, Complex Syst..

[23]  Philippe Collard,et al.  'Royal-Road' Landscapes for a Dual Genetic Algorithm , 1996, ECAI.

[24]  Philippe Collard,et al.  Fitness Distance Correlation in a Dual Genetic Algorithm , 1996, ECAI.

[25]  David E. Goldberg,et al.  Nonstationary Function Optimization Using Genetic Algorithms with Dominance and Diploidy , 1987, ICGA.

[26]  James R. Levenick.  Inserting Introns Improves Genetic Algorithm Success Rate: Taking a Cue from Biology , 1991, ICGA.

[27]  Philippe Collard,et al.  Relational Schemata: A Way to Improve the Expressiveness of Classifiers , 1995, ICGA.

[28]  Luis Mateus Rocha.  Contextual Genetic Algorithms: Evolving Developmental Rules , 1995, ECAL.

[29]  D. E. Goldberg,et al.  Simple Genetic Algorithms and the Minimal, Deceptive Problem , 1987 .

[30]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.