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

Lecture notes in computer science

Quantum-Inspired Evolutionary Algorithms and Its Application to Numerical Optimization Problems

André Vargas Abs da Cruz, Carlos Roberto Hall Barbosa, Marco Aurélio C. Pacheco, Marley M. B. R. Vellasco

Published Jan 1, 2004• 26 citations
Publisher
DOI
10.1007/978-3-540-30499-9_31

Related papers

A Quantum-Inspired Evolutionary Algorithm for Optimization Numerical Problems

Maurizio Fiasché

Lecture notes in computer science•Jan 1, 2012•21 citations

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

Gexiang Zhang, Haina Rong

Lecture notes in computer science•Jan 1, 2007•37 citations

Quantum-Inspired Evolutionary Algorithm for Optimization Problems Approach

Maurizio Fiasché, Morabito Francesco C.

This paper proposes a novel type of quantum-inspired evolutionary algorithm (QiEA) for numerical optimization inspired by the multiple universes principle of quantum computing, which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Numerical optimization problems are an important field of research with several applications in several areas: industrial plant optimization, data mining and many others, and although being successfully used for solving several optimization problems, evolutionary algorithms still present issues that can reduce their performances when faced with task where the evaluation function is computationally intensive. In order to address those issues the QiEA represent the most recent advance in the field of evolutionary computation. This work present some application about combinatorial and numerical optimization problems.

Frontiers in artificial intelligence and applications•Jan 1, 2011•0 citations

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

A Simple Quantum Inspired Evolutionary Algorithm and Its Application to Numerical Optimization Problems

Qing Yang

The paper develops a Simple Quantum Inspired Evolutionary Algorithm and evaluates it on some classical numerical optimization problems and the Need-in-a-haystack problem.The proposed algorithm represents a qubit with a radian and uses only one quantum chromosome.Special operators of rotation and mutation and the method to evaluate a quantum chromosome are presented too.Compared with some other quantum inspired genetic algorithms and the classical genetic algorithm,the novel algorithm shows the outstanding performance and solves the Need-in-a-haystack problem perfectly.

Journal of Wuhan University•Jan 1, 2006•7 citations

Quantum-inspired evolutionary algorithms: a survey and empirical study

Gexiang Zhang

Journal of Heuristics•Jun 28, 2010•297 citations
This paper proposes a novel type of quantum-inspired evolutionary algorithm (QiEA) for numerical optimization inspired by the multiple universes principle of quantum computing, which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Numerical optimization problems are an important field of research with several applications in several areas: industrial plant optimization, data mining and many others, and although being successfully used for solving several optimization problems, evolutionary algorithms still present issues that can reduce their performances when faced with task where the evaluation function is computationally intensive. In order to address those issues the QiEA represent the most recent advance in the field of evolutionary computation. This work present some application about combinatorial and numerical optimization problems.
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.
The paper develops a Simple Quantum Inspired Evolutionary Algorithm and evaluates it on some classical numerical optimization problems and the Need-in-a-haystack problem.The proposed algorithm represents a qubit with a radian and uses only one quantum chromosome.Special operators of rotation and mutation and the method to evaluate a quantum chromosome are presented too.Compared with some other quantum inspired genetic algorithms and the classical genetic algorithm,the novel algorithm shows the outstanding performance and solves the Need-in-a-haystack problem perfectly.