Ecological Model of Virus-Evolutionary Genetic Algorithm

This paper deals with an ecological model on planar gird of a genetic algorithm based on virus theory of evolution (E-VE-GA). In the E-VE-GA, each individual is placed on a planar grid and genetic operators are performed between neighborhoods. The E-VE-GA can self-adaptively change searching ratio between global and local searches. The main operator of the E-VE-GA is reverse transcription and incorporation transmitting local genetic information. The convergence of the E-VE-GA depends on the frequency and localization of the virus infection. In this paper, we apply the E-VE-GA to traveling salesman problems and discuss the coevolution of host and virus populations through the numerical simulation.

[1]  Christopher G. Langton,et al.  Artificial Life , 2019, Philosophical Posthumanism.

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

[3]  H. Ishibuchi,et al.  Formulation of fuzzy flowshop scheduling problems with fuzzy processing time , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[4]  Toshio Fukuda,et al.  Virus-evolutionary genetic algorithm-ecological model on planar grid , 1996, Proceedings of North American Fuzzy Information Processing.

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

[6]  Bernard Manderick,et al.  Fine-Grained Parallel Genetic Algorithms , 1989, ICGA.

[7]  T. Fukuda,et al.  Evolutionary transition on Virus-Evolutionary Genetic Algorithm , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[8]  Luca Maria Gambardella,et al.  Solving symmetric and asymmetric TSPs by ant colonies , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[9]  Robert Hinterding,et al.  Mapping, order-independent genes and the knapsack problem , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[10]  Chuen-Tsai Sun,et al.  Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence [Books in Brief] , 1997, IEEE Transactions on Neural Networks.

[11]  Jan Paredis,et al.  Coevolving Cellular Automata: Be Aware of the Red Queen! , 1997, ICGA.

[12]  Dirk Van Gucht,et al.  The effects of population size, heuristic crossover and local improvement on a genetic algorithm for the traveling salesman problem , 1989 .

[13]  Larry Bull,et al.  Evolutionary Computing in Multi-agent Environments: Operators , 1998, Evolutionary Programming.

[14]  Helio J. C. Barbosa.  A Coevolutionary Genetic Algorithm for a Game Approach to Structural Optimization , 1997, ICGA.

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

[16]  David E. Goldberg,et al.  A Genetic Algorithm for Parallel Simulated Annealing , 1992, PPSN.

[17]  David B. Fogel,et al.  Using fitness distributions to design more efficient evolutionary computations , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[18]  Joseph G. Ecker,et al.  Introduction to Operations Research , 1988, The Mathematical Gazette.

[19]  Jan Paredis,et al.  Coevolutionary computation , 1995 .

[20]  Toshio Fukuda,et al.  Virus-evolutionary genetic algorithm for a self-organizing manufacturing system , 1996 .

[21]  Bernd Freisleben,et al.  A genetic local search algorithm for solving symmetric and asymmetric traveling salesman problems , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[22]  Björn Olsson,et al.  Optimization using a host-parasite model with variable-size distributed populations , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[23]  Shigenobu Kobayashi,et al.  Edge Assembly Crossover: A High-Power Genetic Algorithm for the Travelling Salesman Problem , 1997, ICGA.

[24]  Osamu Katai,et al.  Genetic algorithm involving coevolution mechanism to search for effective genetic information , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[25]  Yuval Davidor,et al.  A Naturally Occurring Niche and Species Phenomenon: The Model and First Results , 1991, ICGA.

[26]  Brian W. Kernighan,et al.  An Effective Heuristic Algorithm for the Traveling-Salesman Problem , 1973, Oper. Res..

[27]  John R. Koza,et al.  Genetic programming II (videotape): the next generation , 1994 .

[28]  Ian C. Parmee,et al.  Co-operative Evolutionary Strategies for Single Component Design , 1997, ICGA.

[29]  Gilbert Syswerda,et al.  A Study of Reproduction in Generational and Steady State Genetic Algorithms , 1990, FOGA.

[30]  David W. Corne,et al.  A Real Coded Genetic Algorithm with an Explorer and an Exploiter Populations , 1997, ICGA.

[31]  Marimuthu Palaniswami,et al.  Computational Intelligence: A Dynamic System Perspective , 1995 .

[32]  Toshio Fukuda,et al.  Virus-evolutionary genetic algorithm-coevolution of planar grid model , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[33]  Jacek M. Zurada,et al.  Computational Intelligence: Imitating Life , 1994 .

[34]  Peter J. Angeline,et al.  Competitive Environments Evolve Better Solutions for Complex Tasks , 1993, ICGA.

[35]  G. Faulkner.  Genetic operators using viral models , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[36]  Toshio Fukuda,et al.  The role of virus infection in virus-evolutionary genetic algorithm , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.