Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Journal of Advancements in Computing Technology

Analysis on the Convergence of Quantum-inspired Evolutionary Algorithms

Ming Chen, Lixin Ding

Published May 31, 2011• 3 citations
Publisher

Abstract

This article deals with the convergence of quantum-inspired evolutionary algorithms with one quantum individual and multiple observations. Applying the theory and analytical techniques in non-homogeneous Markov chain, we obtain the conclusion that quantuminspired evolutionary algorithms could converge in probability under some mild conditions imposed on the probability amplitude of the Q-bit individual. We also analyze three cases from both theoretical and experimental viewpoints. The QEA with rotation gate can not guarantee convergence in theory, while others with modified Q-gates meet the convergence conditions. Numerical results further illustrate feasibleness and effectiveness of the improved algorithms.

DOI
10.4156/ijact.vol3.issue4.22

Related papers

Convergence analysis on a class of quantum-inspired evolutionary algorithms

Shengqiu Yi, Ming Chen, Zhigao Zeng

In this article we focus on the theoretical analysis of quantum-inspired evolutionary algorithms with Hϵgate. Applying the theory and analytical techniques in non-homogeneous Markov chains, we obtain the conclusion that quantum-inspired evolutionary algorithms converge in probability under some mild conditions. Moreover, we estimate the convergence rate relating to parameters of algorithms.

International Conference on Natural Computation (ICNC)•Jul 1, 2011•1 citation

Quantum-Inspired Evolutionary Algorithms With a New Termination Criterion, $H_epsilon $ Gate, and Two-Phase Scheme

K.-H. Han, J.-H. Kim

From recent research on combinatorial optimization of the knapsack problem, quantum-inspired evolutionary algorithm (QEA) was proved to be better than conventional genetic algorithms. To improve the performance of the QEA, this paper proposes research issues on QEA such as a termination criterion, a Q-gate, and a two-phase scheme, for a class of numerical and combinatorial optimization problems. A new termination criterion is proposed which gives a clearer meaning on the convergence of Q-bit individuals. A novel variation operator H/sub /spl epsi// gate, which is a modified version of the rotation gate, is proposed along with a two-phase QEA scheme based on the analysis of the effect of changing the initial conditions of Q-bits of the Q-bit individual in the first phase. To demonstrate the effectiveness and applicability of the updated QEA, several experiments are carried out on a class of numerical and combinatorial optimization problems. The results show that the updated QEA makes QEA more powerful than the previous QEA in terms of convergence speed, fitness, and robustness.

IEEE Transactions on Evolutionary Computation•Apr 1, 2004•484 citations

Quantum-inspired evolutionary algorithm for a class of combinatorial optimization

Kuk-Hyun Han, Jong-Hwan Kim

This paper proposes a novel evolutionary algorithm inspired by quantum computing, called a quantum-inspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, evaluation function, and population dynamics. However, instead of binary, numeric, or symbolic representation, QEA uses a Q-bit, defined as the smallest unit of information, for the probabilistic representation and a Q-bit individual as a string of Q-bits. A Q-gate is introduced as a variation operator to drive the individuals toward better solutions. To demonstrate its effectiveness and applicability, experiments were carried out on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that QEA performs well, even with a small population, without premature convergence as compared to the conventional genetic algorithm.

IEEE Transactions on Evolutionary Computation•Dec 1, 2002•1,592 citations

Advanced Quantum Inspired Evolutionary Optimization Algorithm

Sai Siddhartha Vivek Dhir Rangoju, Om Prakash Patel, Neha Bharill

In this paper, an advanced quantum-inspired evolutionary optimization algorithm (A-QEOA) is proposed. This algorithm is based on the principle of quantum computing such as quantum bit representation and superposition of states. Like other evolutionary algorithms, the quantum-inspired evolutionary algorithm has characteristics of exploration and exploitation. The quantum-inspired evolutionary algorithm achieves exploration using observation process and exploitation using quantum rotation gate. Exploration helps to find the search space for a parameter that needs to be optimized. However, the observation process needs a random number which may sometimes increase the number of iterations to converge the algorithm or may lead to premature convergence. In this paper, we propose an algorithm with an updated observation process that helps to converge the algorithm optimally and helps to get results faster in comparison to the existing quantum inspired evolutionary algorithm (QEA).

International Conference on Emerging Techniques in Computational Intelligence (ICETCI)•Aug 25, 2022•2 citations

On setting the parameters of quantum-inspired evolutionary algorithm for practical application

Kuk-Hyun Han, Jong-Hwan Kim

In this paper, some guidelines for setting the parameters of quantum-inspired evolutionary algorithm (QEA) are presented. QEA is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. However, QEA is not a quantum algorithm, but a novel evolutionary algorithm. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, the evaluation function, and the population dynamics. From recent research on the knapsack problem, the results of QEA are better than those of CGA (conventional GA). Although the performance of QEA is excellent, there is relatively little or no research on the effects of different settings for its parameters. This paper describes some guidelines for setting these parameters. The guidelines are drawn up based on extensive experiments carried out for a class of combinatorial and numerical optimization problems. Through the guidelines, the performance of QEA can be maximized.

IEEE Congress on Evolutionary Computation (CEC)•Jan 1, 2003•69 citations

Amplitude-based coding hybrid quantum-inspired evolutionary algorithm

Shengchao Ding

Coding the individual with amplitude,and applying the crossover operator of classical genetic algorithm to the evolutionary goals of quantum-inspired evolutionary algorithm,a hybrid quantum-inspired evolutionary algorithm is proposed.Combining the self-adaptive rotation,defining a self-adaptive mutation operator with respect to mutation degree,and exchanging the information of the evolutionary goals by crossover operator regularly,the novel algorithm avoids converging prematurely and yields high efficiency.Also the algorithm exceeds QEA and CGA when they solve the numerical optimization problems.The Need-in-a-Haystack problem can be figured out perfectly.Additionally,the algorithm’s computing speed is similar to the classical one.

Computer Engineering and Applications Journal•Jan 1, 2007•0 citations
This article deals with the convergence of quantum-inspired evolutionary algorithms with one quantum individual and multiple observations. Applying the theory and analytical techniques in non-homogeneous Markov chain, we obtain the conclusion that quantuminspired evolutionary algorithms could converge in probability under some mild conditions imposed on the probability amplitude of the Q-bit individual. We also analyze three cases from both theoretical and experimental viewpoints. The QEA with rotation gate can not guarantee convergence in theory, while others with modified Q-gates meet the convergence conditions. Numerical results further illustrate feasibleness and effectiveness of the improved algorithms.
From recent research on combinatorial optimization of the knapsack problem, quantum-inspired evolutionary algorithm (QEA) was proved to be better than conventional genetic algorithms. To improve the performance of the QEA, this paper proposes research issues on QEA such as a termination criterion, a Q-gate, and a two-phase scheme, for a class of numerical and combinatorial optimization problems. A new termination criterion is proposed which gives a clearer meaning on the convergence of Q-bit individuals. A novel variation operator H/sub /spl epsi// gate, which is a modified version of the rotation gate, is proposed along with a two-phase QEA scheme based on the analysis of the effect of changing the initial conditions of Q-bits of the Q-bit individual in the first phase. To demonstrate the effectiveness and applicability of the updated QEA, several experiments are carried out on a class of numerical and combinatorial optimization problems. The results show that the updated QEA makes QEA more powerful than the previous QEA in terms of convergence speed, fitness, and robustness.
This paper proposes a novel evolutionary algorithm inspired by quantum computing, called a quantum-inspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, evaluation function, and population dynamics. However, instead of binary, numeric, or symbolic representation, QEA uses a Q-bit, defined as the smallest unit of information, for the probabilistic representation and a Q-bit individual as a string of Q-bits. A Q-gate is introduced as a variation operator to drive the individuals toward better solutions. To demonstrate its effectiveness and applicability, experiments were carried out on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that QEA performs well, even with a small population, without premature convergence as compared to the conventional genetic algorithm.
In this paper, an advanced quantum-inspired evolutionary optimization algorithm (A-QEOA) is proposed. This algorithm is based on the principle of quantum computing such as quantum bit representation and superposition of states. Like other evolutionary algorithms, the quantum-inspired evolutionary algorithm has characteristics of exploration and exploitation. The quantum-inspired evolutionary algorithm achieves exploration using observation process and exploitation using quantum rotation gate. Exploration helps to find the search space for a parameter that needs to be optimized. However, the observation process needs a random number which may sometimes increase the number of iterations to converge the algorithm or may lead to premature convergence. In this paper, we propose an algorithm with an updated observation process that helps to converge the algorithm optimally and helps to get results faster in comparison to the existing quantum inspired evolutionary algorithm (QEA).
In this paper, some guidelines for setting the parameters of quantum-inspired evolutionary algorithm (QEA) are presented. QEA is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. However, QEA is not a quantum algorithm, but a novel evolutionary algorithm. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, the evaluation function, and the population dynamics. From recent research on the knapsack problem, the results of QEA are better than those of CGA (conventional GA). Although the performance of QEA is excellent, there is relatively little or no research on the effects of different settings for its parameters. This paper describes some guidelines for setting these parameters. The guidelines are drawn up based on extensive experiments carried out for a class of combinatorial and numerical optimization problems. Through the guidelines, the performance of QEA can be maximized.
Coding the individual with amplitude,and applying the crossover operator of classical genetic algorithm to the evolutionary goals of quantum-inspired evolutionary algorithm,a hybrid quantum-inspired evolutionary algorithm is proposed.Combining the self-adaptive rotation,defining a self-adaptive mutation operator with respect to mutation degree,and exchanging the information of the evolutionary goals by crossover operator regularly,the novel algorithm avoids converging prematurely and yields high efficiency.Also the algorithm exceeds QEA and CGA when they solve the numerical optimization problems.The Need-in-a-Haystack problem can be figured out perfectly.Additionally,the algorithm’s computing speed is similar to the classical one.