Semiclassical genetic algorithm with quantum crossover and mutation operations (original) (raw)
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A quantum genetic algorithm with quantum crossover and mutation operations
Quantum Information Processing, 2014
In order for finding a good individual for a given fitness function in the context of evolutionary computing, we introduce a novel semiclassical quantum genetic algorithm. It has both of quantum crossover and quantum mutation procedures unlike conventional quantum genetic algorithms. A complexity analysis shows a certain improvement over its classical counterpart.
A Hybrid Quantum Evolutionary Algorithm
2015
This paper proposes a Hybrid Quantum Evolutionary Algorithm. Quantum computing is capable of processing huge numbers of quantum states simultaneously, in parallel, (“quantum parallelism”), whereas evolutionary computing can only process one chromosome per processor. In theory, QC ought to be able to process upto all possible points in a 2 search space (of N bit chromosomes). An evolutionary algorithm is effectively a guided search algorithm that samples the search space and in general is a slow process. Since QC can examine all or some of 2 points simultaneously, this Quantum Parallelism can be cleverly used to speed the Evolutionary Algorithms. This paper presents a Hybrid Quantum Evolutionary Algorithm that uses the Quantum Parallelism and an other well-known Quantum Algorithm to speed up the Evolutionary Algorithms.
2000
Recent developments in quantum technology have shown that quantum computers can provide a dramatic advantage over classical computers for some algorithms. In particular, a polynomial-time algorithm for factoring, a problem which was previously thought to be hard for classical computers, has recently been developed . Similarly, a quantum algorithm for searching an unsorted database in square root of the time it would take on a classical computer has also been described . Both algorithms rely upon the inherent parallel qualities of quantum computers to achieve their improvement. Unfortunately, not all problems can benefit so dramatically from quantum application . Since most problems of real interest for genetic algorithms (GAs) have a vast search space , it seems appropriate to consider how quantum parallelism can be applied to GAs. In this paper we provide a brief background of quantum computers. We explain how quantum algorithms can provide a fundamental improvement over classical ones for some problems. We present a simple quantum approach to genetic algorithms and analyze its benefits and drawbacks. This is significant because to date there are only a handful of quantum algorithms that take advantage of quantum parallelism ]. Finally, we provide ideas for directions of future research.
Optimization with Quantum Genetic Algorithm
Recent development in quantum technology have shown that quantum computer can provide a dramatic advantage over classical computers for some algorithms. In particular, a polynomial-time algorithm for factoring, a problem which was previously thought to be hard for classical computers, has recently been developed [13]. Similarly, a quantum algorithm searching for unsorted database in square root of time it would take on a classical computer has also been described by Grover [4]-[3]. Both algorithms rely upon the inherent parallelism, superposition and entanglement property of quantum computing to achieve their improvements. Since most problems of real interest for genetic algorithms have a vast search space, it seems appropriate how quantum parallelism can be applied to Genetic Algorithms. In this paper we provide a brief background of quantum computers. We explain why and how quantum algorithms provides a fundamental improvements over classical ones for some problems. Further, we present here the Conventional Genetic Algorithm and the quantum approach of Genetical Algorithms(QGA) as well. The benefits and drawbacks of QGA are also analyzed. Moreover, this paper provides an improved version over the conventional QGA. This improvement originates from the best partial immigration technique applied to the quantum chromosomes. The main objective of the best partial immigration is to consider the string of qubits from the quantum chromosomes having best fitness and transfer the same randomly to the chromosomes of next generation for better mixing. The process is reiterated. To observe the performance the best partial immigration technique we have considered some popular optimization problems and performed the experiment on it. These problems are namely Travelling Salesman Problem(TSP), Binpacking Problem and Vertex Cover Problem. It has been observed that the obtained results outperforms the conventional QGA.
Quantum crossover based quantum genetic algorithm for solving non-linear programming
… and Systems (INFOS …, 2012
Quantum computing proved good results and performance when applied to solving optimization problems. This paper proposes a quantum crossover-based quantum genetic algorithm (QXQGA) for solving non-linear programming. Due to the significant role of mutation function on the QXQGA's quality, a number of quantum crossover and quantum mutation operators are presented for improving the capabilities of searching, overcoming premature convergence, and keeping diversity of population. For calibrating the QXQGA, the quantum crossover and mutation operators are evaluated using relative percentage deviation for selecting the best combination. In addition, a set of non-linear problems is used as benchmark functions to illustrate the effectiveness of optimizing the complexities with different dimensions, and the performance of the proposed QXQGA algorithm is compared with the quantum inspired evolutionary algorithm to demonstrate its superiority.
1 Quantum-Assisted Genetic Algorithm
2019
Genetic algorithms, which mimic evolutionary processes to solve optimization problems, can be enhanced by using powerful semi-local search algorithms as mutation operators. Here, we introduce reverse quantum annealing, a class of quantum evolutions that can be used for performing families of quasi-local or quasi-nonlocal search starting from a classical state, as novel sources of mutations. Reverse annealing enables the development of genetic algorithms that use quantum fluctuation for mutations and classical mechanisms for the crossovers—we refer to these as Quantum-Assisted Genetic Algorithms (QAGAs). We describe a QAGA and present experimental results using a D-Wave 2000Q quantum annealing processor. On a set of spin-glass inputs, standard (forward) quantum annealing finds good solutions very quickly but struggles to find global optima. In contrast, our QAGA proves effective at finding global optima for these inputs. This successful interplay of nonlocal classical and quantum flu...
Survey of Quantum-Inspired Evolutionary Algorithms
Abstract. This paper presents a concise survey of a new class of metaheuristics, drawing their inspiration from both: biological evolution and unitary evolution of quantum systems. In the first part of the paper, general concepts behind quantum-inspired evolutionary algorithms have been presented. In the second part, a state of the art of this field has been discussed and a literature review has been conducted.
A versatile quantum-inspired evolutionary algorithm
2007 IEEE Congress on Evolutionary Computation, 2007
This study points out some weaknesses of existing Quantum-Inspired Evolutionary Algorithms (QEA) and explains in particular how hitchhiking phenomenons can slow down the discovery of optimal solutions and encourage premature convergence. A new algorithm, called Versatile Quantuminspired Evolutionary Algorithm (vQEA), is proposed. With vQEA, the attractors moving the population through the search space are replaced at every generation without considering their fitness. The new algorithm is much more reactive. It always adapts the search toward the last promising solution found thus leading to a smoother and more efficient exploration. In this paper, vQEA is tested and compared to a Classical Genetic Algorithm CGA and to a QEA on several benchmark problems. Experiments have shown that vQEA performs better than both CGA and QEA in terms of speed and accuracy. It is a highly scalable algorithm as well. Finally, the properties of the vQEA are discussed and compared to Estimation of Distribution Algorithms (EDA).
Quantum-enhanced selection operators for evolutionary algorithms
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Genetic algorithms have unique properties which are useful when applied to black box optimization. Using selection, crossover, and mutation operators, candidate solutions may be obtained without the need to calculate a gradient. In this work, we study results obtained from using quantum-enhanced operators within the selection mechanism of a genetic algorithm. Our approach frames the selection process as a minimization of a binary quadratic model with which we encode fitness and distance between members of a population, and we leverage a quantum annealing system to sample low energy solutions for the selection mechanism. We benchmark these quantum-enhanced algorithms against classical algorithms over various black-box objective functions, including the OneMax function, and functions from the IOHProfiler library for black-box optimization. We observe a performance gain in average number of generations to convergence for the quantum-enhanced elitist selection operator in comparison to classical on the OneMax function. We also find that the quantum-enhanced selection operator with non-elitist selection outperform benchmarks on functions with fitness perturbation from the IOHProfiler library. Additionally, we find that in the case of elitist selection, the quantum-enhanced operators outperform classical benchmarks on functions with varying degrees of dummy variables and neutrality. CCS CONCEPTS • Computing methodologies → Discrete space search; • Computer systems organization → Quantum computing.