An Improved Quantum Genetic Algorithm and Its Application

Zhang, Gexiang; Jin, Weidong; Li, Na

doi:10.1007/3-540-39205-x_75

Cited by 24 publications

(21 citation statements)

References 1 publication

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“…Using a Markov chain method, the convergence of bQIEAn was analyzed mathematically in Zhang et al 2003b). The applicability of bQIEAn to physical distribution vehicle routing, FIR and IIR digital filter design and time-frequency atom decomposition was also discussed in Gao et al (2006), Wang et al (2007b), Zhang et al (2003aZhang et al ( , 2003c, Zhang andRong (2006, 2007b). Table 4 summarizes the work done on bQIEAn.…”

Section: Bqieanmentioning

confidence: 99%

Quantum-inspired evolutionary algorithms: a survey and empirical study

2010

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Quantum-inspired evolutionary algorithms, one of the three main research areas related to the complex interaction between quantum computing and evolutionary algorithms, are receiving renewed attention. A quantum-inspired evolutionary algorithm is a new evolutionary algorithm for a classical computer rather than for quantum mechanical hardware. This paper provides a unified framework and a comprehensive survey of recent work in this rapidly growing field. After introducing of the main concepts behind quantum-inspired evolutionary algorithms, we present the key ideas related to the multitude of quantum-inspired evolutionary algorithms, sketch the differences between them, survey theoretical developments and applications that range from combinatorial optimizations to numerical optimizations, and compare the advantages and limitations of these various methods. Finally, a small comparative study is conducted to evaluate the performances of different types of quantum-inspired evolutionary algorithms and conclusions are drawn about some of the most promising future research developments in this area.

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Section: Bqieanmentioning

confidence: 99%

Quantum-inspired evolutionary algorithms: a survey and empirical study

2010

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“…Instead of numeric, binary or symbol representation, Q-bit representation is used to represent the individuals of population in RQEA [1][2][3][4]. The probability amplitude of a Q-bit is defined firstly.…”

Section: Rqeamentioning

confidence: 99%

“…QEA inherits the structure and probabilistic search way of conventional genetic algorithm (CGA), and some concepts and operations of quantum computing, such as quantum-inspired bit (Q-bit), quantum-inspired gate (Q-gate) and quantum operators including superposition, entanglement, interference and measurement [1,2]. Up to now, as a better optimization method than CGA, QEA has been used in several applications of knapsack problem [2,3], digital filter design [4], feature selection [1]. Extensively experimental results manifest its advantages of good global search capability, rapid convergence and speediness [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

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Real-Observation Quantum-Inspired Evolutionary Algorithm for a Class of Numerical Optimization Problems

Zhang

Rong

2007

Computational Science – ICCS 2007

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Abstract. This paper proposes a real-observation quantum-inspired evolutionary algorithm (RQEA) to solve a class of globally numerical optimization problems with continuous variables. By introducing a real observation and an evolutionary strategy, suitable for real optimization problems, based on the concept of Q-bit phase, RQEA uses a Q-gate to drive the individuals toward better solutions and eventually toward a single state corresponding to a real number varying between 0 and 1. Experimental results show that RQEA is able to find optimal or closeto-optimal solutions, and is more powerful than conventional real-coded genetic algorithm in terms of fitness, convergence and robustness.

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“…Based on the concepts of quantum computing, QGA falls into the latest category of unconventional computation. Due to some outstanding advantages such as good global search capability, rapid convergence and short computing time [10][11][12], QGA is able to accelerate greatly the process of searching the most satisfactory time-frequency atom in each iteration of TFAD. In Section 2, the detailed algorithm of QGA based TFAD is described.…”

Section: Introductionmentioning

confidence: 99%

Quantum-Inspired Genetic Algorithm Based Time-Frequency Atom Decomposition

Zhang

Rong

2007

Computational Science – ICCS 2007

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Abstract. The main problem of time-frequency atom decomposition (TFAD) lies in an extremely high computational load. This paper presents a fast implementation method based on quantum-inspired genetic algorithm (QGA). Instead of finding the optimal atom in greedy implementation algorithm, this method is to search a satisfactory atom in every iteration of TFAD. Making full use of QGA's advantages such as good global search capability, rapid convergence and short computing time, the method reduces greatly the computational load of TFAD. Experiments conducted on radar emitter signals verify the effectiveness and practicality of the introduced method.

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An Improved Quantum Genetic Algorithm and Its Application

Cited by 24 publications

References 1 publication

Quantum-inspired evolutionary algorithms: a survey and empirical study

Quantum-inspired evolutionary algorithms: a survey and empirical study

Real-Observation Quantum-Inspired Evolutionary Algorithm for a Class of Numerical Optimization Problems

Quantum-Inspired Genetic Algorithm Based Time-Frequency Atom Decomposition

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