A novel quantum evolutionary algorithm and its application

In this paper, an algorithm - the quantum evolutionary algorithm (QEA) is introduced. It is characterized by a representation of quantum chromosomes, quantum mutation and quantum crossover. Its advantages lies on better diversity of individuals, effective guidance of mutation and the avoidance of prematurity by crossover. Some simulations are given to illustrate its efficiency and better performance than its counterpart. Finally, we applied it to the multi-user detection in DS-CDMA, and good results are attained.

[1]  R. Feynman.  Quantum mechanical computers , 1986 .

[2]  R. Feynman.  Simulating physics with computers , 1999 .

[3]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[4]  Jong-Hwan Kim,et al.  Parallel quantum-inspired genetic algorithm for combinatorial optimization problem , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[5]  Jong-Hwan Kim,et al.  On setting the parameters of quantum-inspired evolutionary algorithm for practical application , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[6]  Heinz Mühlenbein,et al.  Parallel Genetic Algorithms in Combinatorial Optimization , 1992, Computer Science and Operations Research.

[7]  Günter Rudolph,et al.  Convergence analysis of canonical genetic algorithms , 1994, IEEE Trans. Neural Networks.

[8]  Peter M. Todd,et al.  Designing Neural Networks using Genetic Algorithms , 1989, ICGA.

[9]  David E. Goldberg,et al.  Control system optimization using genetic algorithms , 1992 .

[10]  John J. Grefenstette,et al.  Genetic Algorithms for the Traveling Salesman Problem , 1985, ICGA.

[11]  Noam Nisan,et al.  Quantum circuits with mixed states , 1998, STOC '98.

[12]  Jong-Hwan Kim,et al.  Quantum-inspired evolutionary algorithms with a new termination criterion, H/sub /spl epsi// gate, and two-phase scheme , 2004, IEEE Transactions on Evolutionary Computation.

[13]  Rick L. Riolo,et al.  Modeling Simple Human Category Learning with a Classifier System , 1991, International Conference on Genetic Algorithms.

[14]  G. W. Greenwood,et al.  Finding solutions to NP problems: philosophical differences between quantum and evolutionary search algorithms , 2000, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[15]  Hans-Paul Schwefel,et al.  TWO-PHASE NOZZLE AND HOLLOW CORE JET EXPERIMENTS. , 1970 .

[16]  Gexiang Zhang,et al.  A novel parallel quantum genetic algorithm , 2003, Proceedings of the Fourth International Conference on Parallel and Distributed Computing, Applications and Technologies.

[17]  Ajit Narayanan,et al.  Quantum-inspired genetic algorithms , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[18]  Alexander A. Stepanov,et al.  Generic Programming , 1988, ISSAC.

[19]  Guy Albert Dumont,et al.  System identification and control using genetic algorithms , 1992, IEEE Trans. Syst. Man Cybern..

[20]  Jong-Hwan Kim,et al.  Quantum-inspired evolutionary algorithm for a class of combinatorial optimization , 2002, IEEE Trans. Evol. Comput..

[21]  Lov K. Grover.  A framework for fast quantum mechanical algorithms , 1997, STOC '98.

[22]  Peter W. Shor,et al.  Algorithms for quantum computation: discrete logarithms and factoring , 1994, Proceedings 35th Annual Symposium on Foundations of Computer Science.

[23]  Andrew Whinston,et al.  Applying Adaptive Credit Assignment Algorithm for the Learning Classifier System Based upon the Genetic Algorithm , 1992 .