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International Journal of Advanced Pervasive and Ubiquitous Computing

Multiple Quantum Spaces Based Genetic Coding Method

Tao Gao

Published Apr 1, 2014• 0 citations
Publisher

Abstract

Quantum genetic coding method plays an important role in improving the efficiency of optimization algorithm. The existing quantum genetic algorithm has some defects, that quantum encoding scheme is tend to reduce the stability, so that the algorithm is prone to premature convergence and falls into local minima. Therefore, the chain of multiple genes encoding scheme is used to extend in the multi-dimensional space for improving this algorithm. By function extremum and simulation of neural network weights optimization, according to the characteristics of qubits and the normalization condition, double and triple chain binding coding schemes are proposed. By experiments on multiple genes encoding scheme chain, the performance of the algorithm is tested. It shows that the algorithm can get better results by increasing the higher accuracy of solution chain genes. It is an effective strategy to improve the performance of genetic coding.

DOI
10.4018/ijapuc.2014040104

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Quantum genetic coding method plays an important role in improving the efficiency of optimization algorithm. The existing quantum genetic algorithm has some defects, that quantum encoding scheme is tend to reduce the stability, so that the algorithm is prone to premature convergence and falls into local minima. Therefore, the chain of multiple genes encoding scheme is used to extend in the multi-dimensional space for improving this algorithm. By function extremum and simulation of neural network weights optimization, according to the characteristics of qubits and the normalization condition, double and triple chain binding coding schemes are proposed. By experiments on multiple genes encoding scheme chain, the performance of the algorithm is tested. It shows that the algorithm can get better results by increasing the higher accuracy of solution chain genes. It is an effective strategy to improve the performance of genetic coding.
Quantum coding plays an important role in quantum genetic algorithm and affects the optimizing efficiency of algorithm, However, there are some defects in existing quantum genetic algorithm: the quantum coding scheme reduces the stability of the algorithm, making the algorithm easily occurs premature convergence and falls into local extremums.Therefore, coding scheme of multi gene chain is extended in a four dimensional space and multidimensional space based on an improved algorithm.Through simulation experiments of function extremum, combine double and triplestranded coding scheme, in accordance with the characteristics of qubits and the normalization condition.Through the experiment, study the effect of multiple genes encoding scheme chain on the performance of the algorithm.The results show that the optimal results of the algorithm can be better and the precision of solution can be higher by increasing the genetic chain.It is an effective strategy to improve the performance of algorithm.
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To address training of process neural networks based on the orthogonal basis expansion, a double chains quantum genetic algorithm based on the probability amplitudes of quantum bits is proposed. In this method, the probability amplitudes of each qubit are regarded as two paratactic genes, each chromosome contains two gene chains, and each of gene chains represents an optimization solution. The number of genes is determined by the number of weight parameters. Taking each qubit in the optimal chromosome as the goal, individuals are updated by quantum rotation gates, and mutated by quantum non-gates to increase the diversity of population. Taking the pattern classification of two groups of two-dimensional trigonometric functions as an example, the simulation results show that the proposed method is effective and efficient.
The double chains quantum genetic algorithm based on real coded and gradient of objective function increases the diversity of population,expands the search field about space,accelerates the algorithm evolutionary process,avoids the premature convergence phenomenon,is an effective optimization algorithm,but does prove theoretically convergence.This paper gives the corresponding theorem on the basis of previous work,theoretically proves the convergence of the algorithm by the theorem,and discusses the effects of the quantum coding and quantum rotation gate in terms of the algorithm convergence and optimization efficiency by simulation example.Results show that his research enriches and improves the double chains quantum genetic theory.
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A quantum genetic algorithm based on real number encoding and gradient of object function is presented. In this study, chromosomes are comprised of quantum bits encoded by real number. Chromosomes are renovated by quantum rotating gates and mutated by quantum non - gate. For direction of rotating angle of rotating gate, a convenient method is shown. The gradients of object function are utilized in choosing the value of rotating angle. In this method each quantum bit is regarded as two coordinate genes, each chromosome contains two chain of genes, each chain of genes represents a optimization result. Therefore, a double searching space is acquired for the same number of chromosomes. Finally the availability of the approach is illustrated by two application examples of function extremum and weighting optimization of neural networks.