Quantum-Inspired Evolutionary Algorithm for Numerical Optimization

A versatile quantum-inspired evolutionary algorithm

“…Even if a Qindividual is unchanged, its fitness is reevaluated every generation according to a realization of the distribution. For that purpose, each Q i is first measured to form a binary individual C i in a collapsed state 2 and then the fitness evaluation takes place. In the sense of classical EA, Q i is the genotype while C i is the phenotype of a given individual.…”

“…Indeed, every generation C i and A i are compared in terms of both fitness and bit values. If A i is better than C i and if their bit values differ, a quantum 2 The way a Q i collaspes is described in [7] gate operator is applied on the corresponding Qbits of Q i . Thus the distribution Q i is slightly moved toward a given solution A i of the search space.…”

“…In the last ten years QEA received a lot of attention and have already demonstrated their superiority compared to classical EA for solving complex benchmark problems such as combinatorial [7], numerical [9], [2] and multiobjective optimization [17], as well as real world problems namely disk allocation method [14], face detection [12], rigid image registration [3], training of multi layer perceptron [18], signal processing [15] and clustering of gene expression data [21]. However QEA are still poorly understood and their integration into the theory of Evolutionary Computation is missing.…”

Neuroevolutionary learning in nonstationary environments

“…To deal with this issue, an interesting and still littleexplored strategy in the literature related to neuroevolutionary models is the quantum-inspired evolutionary algorithms. This is a class of evolutionary algorithms developed to achieve better performance in computationally intensive problems, inspired by quantum computing principles [17,18,2,39,52,8]. One of the main advantages of the quantum-inspired evolutionary models is that good solutions are obtained with the smallest possible number of evaluations.…”

“…One of the main advantages of the quantum-inspired evolutionary models is that good solutions are obtained with the smallest possible number of evaluations. This class of algorithms has been previously used in the literature to solve combinatorial and numerical optimization problems, based on binary [18,39] and real representations [2,39,52], providing better results and using less computational effort than classical genetic algorithms [47]. Applied to neural network ensembles, quantum-inspired evolutionary algorithms can be used to model the neural networks and to determine the voting weights for each ensemble member.…”