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 Conference on Intelligent Systems and Knowledge Engineering (ISKE)

A New Parallel Quantum Genetic Algorithm with Probability-Gate and Its Probability Analysis

MA Shu-xia, Weidong Jin

Published Jan 1, 2007• 5 citations
Publisher

Abstract

In this paper, a novel Quantum Genetic Algorithm (QGA) called Parallel Probability-gate Quantum Genetic Algorithm (PPQGA) is proposed.The main points of PPQGA are that a new chromosome representation called qubit representation, a new updating gate called probability-gate and a novel evolutionary strategy being used in update procedure are introduced.Based on the concepts and principles of quantum computing introduced, PPQGA is characterized by rapid convergence, good global search capability and the ability of possessing exploitation simultaneously.The experimental results of several special functions show that PPQGA can speedup the migration of the top individuals of subpopulations and is superior to other several genetic algorithms greatly in quality and efficiency.

DOI
10.2991/iske.2007.260

Related papers

Parallel quantum-inspired genetic algorithm for combinatorial optimization problem

Kuk-Hyun Han, Kui-Hong Park, Ci-Ho Lee +1 more

This paper proposes a new parallel evolutionary algorithm called parallel quantum-inspired genetic algorithm (PQGA). Quantum-inspired genetic algorithm (QGA) is based on the concept and principles of quantum computing such as qubits and superposition of states. Instead of binary, numeric, or symbolic representation, by adopting the qubit chromosome as a representation, QGA can represent a linear superposition of solutions due to its probabilistic representation. QGA is suitable for parallel structures because of rapid convergence and good global search capability. That is, QGA is able to possess the two characteristics of exploration and exploitation simultaneously. The effectiveness and the applicability of PQGA are demonstrated by experimental results on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that PQGA is superior to QGA as well as other conventional genetic algorithms.

IEEE Congress on Evolutionary Computation (CEC)•Nov 13, 2002•193 citations

A novel genetic algorithm based on the quantum chromosome

Shuyuan Yang, Fang Liu

The genetic algorithm is an efficient optimization tool for its independence of problems, intrinsic parallelism and inherent learning capacity; however, it has some disadvantages such as slow convergence and prematurity. In this paper, a novel algorithm, called the quantum genetic algorithm—QGA, is proposed based on the combination of the quantum theory with the evolutionary theory. By adopting the qubit chromosome as a representation, QGA can represent a linear superposition of solutions due to its probabilistic representation. The random observation simulating the quantum collapse can bring diverse individuals, and the evolution of quantum chromosome can also pilot the evolution. So it has better diversity than the classical genetic algorithm. Rapid convergence and good global search capacity characterize the performance of QGA. The paper not only proves the global convergence of the QGA, but also some simulation experiments are shown to prove its superiority over other algorithms.

Journal of Xidian University•Jan 1, 2004•13 citations

A Novel Quantum Genetic Algorithm and Its Application

Gexiang Zhang

This paper proposes a novel quantum genetic algorithm (NQGA),whose core is that the strategies of updating quantum gate using qubit phase comparison approach and adjusting search grid adaptively are introduced.NQGA is characterized by rapid convergence,good global search capability and short computing time.Test results of complex functions and application example demonstrate NQGA is superior to conventional genetic algorithms in quality and efficiency.

Dianzi xuebao•Jan 1, 2004•49 citations

Genetic quantum algorithm and its application to combinatorial optimization problem

Kuk-Hyun Han, Jong-Hwan Kim

This paper proposes a novel evolutionary computing method called a genetic quantum algorithm (GQA). GQA is based on the concept and principles of quantum computing such as qubits and superposition of states. Instead of binary, numeric, or symbolic representation, by adopting qubit chromosome as a representation GQA can represent a linear superposition of solutions due to its probabilistic representation. As genetic operators, quantum gates are employed for the search of the best solution. Rapid convergence and good global search capability characterize the performance of GQA. The effectiveness and the applicability of GQA are demonstrated by experimental results on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that GQA is superior to other genetic algorithms using penalty functions, repair methods and decoders.

IEEE Congress on Evolutionary Computation (CEC)•Nov 7, 2002•672 citations

Multi-Universe Parallel Quantum Genetic Algorithm

Shi Liang

This paper first proposes a novel Multi-Universe Parallel Quantum Genetic Algorithm (MPQGA) and proves its global convergence in theory.In the algorithm,all individuals are divided into some independent sub-colonies,called universes,according to their definite topological structure.Individuals in a universe are represented by multi-state gene qubits.In the individual's updating,the general quantum rotation gate strategy and dynamic adjusting rotation angle mechanism are applied to accelerate convergence.Quantum NOT gate is used to realize quantum mutation to avoid premature convergence.Each universe evolving independently enlarges the search space.Information among the universes is exchanged by adopting the best emigration and the quantum crossover operation for the improvement of search efficiency.Then it puts forward a new Blind Source Separation (BSS) method based on the combination of MPQGA and Independent Component Analysis (ICA).The simulation results show that the efficiency of the new BSS method is obviously higher than that of the Conventional Genetic Algorithm (CGA) and the Quantum Genetic Algorithm (QGA).

Dianzi xuebao•Jan 1, 2004•5 citations

A genetic algorithm based on quantum chromosome

Shuyuan Yang, Min Wang, Licheng Jiao

In this paper, we propose a novel genetic algorithm based on the quantum chromosome - QGA. We adopt quantum chromosome to represent a linear superposition of solutions, after the observation that simulates the quantum collapse. The algorithm has better diversity than its counterpart. In addition, the quantum evolutionary operator - quantum mutation is constructed, which is used to speed up the convergence. Rapid convergence and global search capacity characterize the performance of QGA. The paper also gives some simulation experiments to prove its superiority over the classical genetic algorithm.

International Conference on Signal Processing•Jun 28, 2005•19 citations
In this paper, a novel Quantum Genetic Algorithm (QGA) called Parallel Probability-gate Quantum Genetic Algorithm (PPQGA) is proposed.The main points of PPQGA are that a new chromosome representation called qubit representation, a new updating gate called probability-gate and a novel evolutionary strategy being used in update procedure are introduced.Based on the concepts and principles of quantum computing introduced, PPQGA is characterized by rapid convergence, good global search capability and the ability of possessing exploitation simultaneously.The experimental results of several special functions show that PPQGA can speedup the migration of the top individuals of subpopulations and is superior to other several genetic algorithms greatly in quality and efficiency.
This paper proposes a new parallel evolutionary algorithm called parallel quantum-inspired genetic algorithm (PQGA). Quantum-inspired genetic algorithm (QGA) is based on the concept and principles of quantum computing such as qubits and superposition of states. Instead of binary, numeric, or symbolic representation, by adopting the qubit chromosome as a representation, QGA can represent a linear superposition of solutions due to its probabilistic representation. QGA is suitable for parallel structures because of rapid convergence and good global search capability. That is, QGA is able to possess the two characteristics of exploration and exploitation simultaneously. The effectiveness and the applicability of PQGA are demonstrated by experimental results on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that PQGA is superior to QGA as well as other conventional genetic algorithms.
The genetic algorithm is an efficient optimization tool for its independence of problems, intrinsic parallelism and inherent learning capacity; however, it has some disadvantages such as slow convergence and prematurity. In this paper, a novel algorithm, called the quantum genetic algorithm—QGA, is proposed based on the combination of the quantum theory with the evolutionary theory. By adopting the qubit chromosome as a representation, QGA can represent a linear superposition of solutions due to its probabilistic representation. The random observation simulating the quantum collapse can bring diverse individuals, and the evolution of quantum chromosome can also pilot the evolution. So it has better diversity than the classical genetic algorithm. Rapid convergence and good global search capacity characterize the performance of QGA. The paper not only proves the global convergence of the QGA, but also some simulation experiments are shown to prove its superiority over other algorithms.
This paper proposes a novel evolutionary computing method called a genetic quantum algorithm (GQA). GQA is based on the concept and principles of quantum computing such as qubits and superposition of states. Instead of binary, numeric, or symbolic representation, by adopting qubit chromosome as a representation GQA can represent a linear superposition of solutions due to its probabilistic representation. As genetic operators, quantum gates are employed for the search of the best solution. Rapid convergence and good global search capability characterize the performance of GQA. The effectiveness and the applicability of GQA are demonstrated by experimental results on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that GQA is superior to other genetic algorithms using penalty functions, repair methods and decoders.
This paper first proposes a novel Multi-Universe Parallel Quantum Genetic Algorithm (MPQGA) and proves its global convergence in theory.In the algorithm,all individuals are divided into some independent sub-colonies,called universes,according to their definite topological structure.Individuals in a universe are represented by multi-state gene qubits.In the individual's updating,the general quantum rotation gate strategy and dynamic adjusting rotation angle mechanism are applied to accelerate convergence.Quantum NOT gate is used to realize quantum mutation to avoid premature convergence.Each universe evolving independently enlarges the search space.Information among the universes is exchanged by adopting the best emigration and the quantum crossover operation for the improvement of search efficiency.Then it puts forward a new Blind Source Separation (BSS) method based on the combination of MPQGA and Independent Component Analysis (ICA).The simulation results show that the efficiency of the new BSS method is obviously higher than that of the Conventional Genetic Algorithm (CGA) and the Quantum Genetic Algorithm (QGA).
In this paper, we propose a novel genetic algorithm based on the quantum chromosome - QGA. We adopt quantum chromosome to represent a linear superposition of solutions, after the observation that simulates the quantum collapse. The algorithm has better diversity than its counterpart. In addition, the quantum evolutionary operator - quantum mutation is constructed, which is used to speed up the convergence. Rapid convergence and global search capacity characterize the performance of QGA. The paper also gives some simulation experiments to prove its superiority over the classical genetic algorithm.