A Study On Improved Quantum Genetic Algorithm

Quantum Genetic Algorithms is a new optimum method that combines quantum computation with Genetic Algorithms, It appears strong life-force and be valuable for research. Quantum computation absorbed many essential characters of quantum mechanics, which improved the computation efficiency, and become a brand new model of computation. A. Narayanan & M. Moore put forward the concept of Quantum Genetic in his article 'Quantum-inspired genetic algorithm', K. H. Han, K. H. Park, C. H. Lee & J. H. Kim applied Quantum Genetic Algorithms to solve the problem of Combinational Optimization and have got a good effect. But some research indicate that QGA is not fit to solve the optimization problems of continuous function, especially the optimization problems of muti-peaked continuous functions. So some improved Quantum Genetic Algorithms are put forward, the performance of the algorithms are better.This dissertation includes six chapters. The first chapter is introduction, It briefly introduces the current development of the Modern Optimized Method and quantum computation and Quantum Genetic Algorithms. Through analysing the excellence of quantum computation and Quantum Genetic Algorithms, it introduces the quick development reason of Quantum Genetic Algorithms. The second chapter briefly introduces quantum computation, fitness function of quantum computation, genetic operation of quantum computation. The third chapter briefly introduces Quantum Genetic Algorithms, the basic knowledge of quantum mechanics, quantum logical gate, the basic expressing method of quantum bit, operation renewal of quantum gate, and flow chart of Quantum Genetic Algorithms. The fourth chapter presents Block Quantum Genetic Algorithms, by layering and blocking the individuals, and every layer uses different quantum computation, and trying the solution space through different ways, by using the methods such as layering and blocking, guarantees the diversity of chromosome, and keeps the diversity of solution too. The fifth chapter presents Quantum Genetic Algorithms based on chaos, chaos can pass through all the states without repetition, So it has the characteristics of ergodicity and randomness and regularity ,its sensitivity to initial value makes its search avoid dropping into local optimum. So the integrate of two algorithms can behave better. The sixth chapter presents Quantum Genetic Algorithm based...