Transposition versus crossover: an empirical study

Genetic algorithms are adaptive systems biologically motivated which have been used to solve different problems. Since Holland's proposals back in 1975, two main genetic operators, crossover and mutation, have been explored with success. Nonetheless, nature presents many other mechanisms of genetic recombination, based on phenomena like gene insertion, duplication or movement. The aim of this paper is to study one of these mechanisms: transposition. Transposition is a context-sensitive operator that promotes gene movement intra or inter chromosomes. This work presents an empirical study of the genetic algorithm performance, being the traditional crossover operator replaced by transposition. Such empirical study, based on an extensive set of test functions, shows that, under certain circumstances, transposition allows the GA to achieve higher quality solutions.

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

[2]  Anabela Simões,et al.  Transposition: A Biological-Inspired Mechanism to Use with Genetic Algorithms , 1999, ICANNGA.

[3]  Robert G. Reynolds,et al.  Some Interesting Test Functions for Evaluating Evolutionary Programming Strategies , 1995 .

[4]  Anthony V. Sebald,et al.  Some Interesting Test Funcitons for Evaluating Evolutionary Programming Strategies , 1995, Evolutionary Programming.

[5]  Lawrence Davis,et al.  Adapting Operator Probabilities in Genetic Algorithms , 1989, ICGA.

[6]  Peter Nordin,et al.  Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications , 1998 .

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

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

[9]  Gilbert Syswerda,et al.  Uniform Crossover in Genetic Algorithms , 1989, ICGA.

[10]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[11]  E. Costa,et al.  Enhancing transposition performance , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[12]  Takeshi Furuhashi,et al.  Bacterial evolutionary algorithm for fuzzy system design , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[13]  L. Darrell Whitley,et al.  Genetic Operators, the Fitness Landscape and the Traveling Salesman Problem , 1992, PPSN.

[14]  Larry J. Eshelman,et al.  On Crossover as an Evolutionarily Viable Strategy , 1991, ICGA.

[15]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[16]  Yuval Davidor.  Analogous Crossover , 1989, ICGA.

[17]  B. Bainbridge,et al.  Genetics , 1981, Experientia.

[18]  L. Darrell Whitley,et al.  Building Better Test Functions , 1995, ICGA.

[19]  Kenneth A. De Jong,et al.  An Analysis of Multi-Point Crossover , 1990, FOGA.

[20]  Y. Uchikawa,et al.  A New Approach to Genetic Based Machine Learning and an Efficient Finding of Fuzzy Rules - Proposal of Nagoya Approach - , 1994, IEEE/Nagoya-University World Wisepersons Workshop.

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

[22]  Yoshiki Uchikawa,et al.  A study on the discovery of relevant fuzzy rules using pseudobacterial genetic algorithm , 1999, IEEE Trans. Ind. Electron..

[23]  Yoshiki Uchikawa,et al.  Knowledge Acquisition of Fuzzy Control Rules for Mobile Robots Using DNA Coding Method and Pseudo-Bacterial GA , 1996, SEAL.

[24]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[25]  T. Furuhashi,et al.  A study on fuzzy rules discovery using Pseudo-Bacterial Genetic Algorithm with adaptive operator , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[26]  Zbigniew Michalewicz,et al.  Genetic Algorithms Plus Data Structures Equals Evolution Programs , 1994 .

[27]  Kennetb A. De.  Genetic Algorithms Are NOT Function Optimizers , 1992 .

[28]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

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

[30]  Melanie Mitchell,et al.  Genetic Algorithms and Artificial Life , 1994, Artificial Life.

[31]  J. David Schaffer,et al.  Proceedings of the third international conference on Genetic algorithms , 1989 .

[32]  Kenneth A. De Jong,et al.  Genetic Algorithms are NOT Function Optimizers , 1992, FOGA.

[33]  Patrik D'haeseleer,et al.  Context preserving crossover in genetic programming , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[34]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..