Evolutionary programming (EP) is an efficient algorithm in solving optimization problems, but the criterion EP is of torpid convergence. In this paper, a novel kind of algorithm, the quantum evolutionary programming (QEP), is proposed based on the combination of quantum theory with evolutionary theory. It is a kind of evolutionary programming with the form of quantum chromosome, whose core lies on the concept and principles of quantum computing, such as quantum bit and superposition of states. Using quantum mutation, we can make full use of the information of the current best individual to perform the next search, so QEP has rapid convergence and global searching capacity. Some simulation experiments are also given to prove its superiority over its counterpart.
Evolutionary programming (EP) is an efficient algorithm in solving optimization problems, but the criterion EP is of torpid convergence. In this paper, a novel kind of algorithm, the quantum evolutionary programming (QEP), is proposed based on the combination of quantum theory with evolutionary theory. It is a kind of evolutionary programming with the form of quantum chromosome, whose core lies on the concept and principles of quantum computing, such as quantum bit and superposition of states. Using quantum mutation, we can make full use of the information of the current best individual to perform the next search, so QEP has rapid convergence and global searching capacity. Some simulation experiments are also given to prove its superiority over its counterpart.
In this paper,a novel kind of algorithm,the quantum evolutionary strategies QES,is proposed based on the combination of quantum theory and evolutionary theory.It is a kind of evolutionary strategies with the form of quantum chromosome,whose core lies on the concept and principles of quantum computing,such as qubits and superposition of states.By using qubit mutation,we can make full use of the information of the currently best individual to perform the next search,and use whole interference to avoid prematurity,so it has rapid convergence and good global search capacity.The paper not only proves the global convergence of the QES,but some simulated experimentats are given to prove its superiority to other 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.
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.
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.
Quantum evolutionary algorithm is a novel and efficient algorithm which inspired by the ideas of quantum computing,and it has excellent results on the computational efficiency and avoid falling into local extremum problems.Therefore,the combination of quantum mechanics and intelligent optimization algorithms will further expand the application areas of intelligent optimization algorithms to improve the optimization algorithm problem-solving ability.For this,the introduction of quantum computation to the differential evolution algorithm,we proposed a new evolutionary algorithm-quantum differential evolution algorithm.This method is the probability of quantum bits used in the real-coded chromosomes,with the quantum mutation,quantum crossover and quantum choose operation to achieve the update of chromosomal location,and using the quantum NOT gate to swap two probability amplitude of quantum bits,it can prevent the premature algorithm,while the algorithm converges faster.The function extreme value and TSP problems as an example to verify the effectiveness of the proposed algorithm.