Quantum-Inspired Evolutionary Algorithm for Numerical Optimization

Cruz, André Vargas Abs da; Vellasco, Marley; Pacheco, Marco Aurélio Cavalcanti

doi:10.1007/978-3-540-73297-6_2

Cited by 43 publications

(82 citation statements)

References 10 publications

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“…No further attention was paid to QIEAs until a practical algorithm was proposed by Kim (2000, 2002), but they are now viewed as an emergent theme in evolutionary computation. Albeit various variants of QIEA have been presented in the literature, they can be categorized into three types: binary observation QIEA (bQIEA) (Han and Kim 2000, real observation QIEA (rQIEA) (Zhang and Rong 2007c;Liu et al 2008) and QIEA-like algorithms (iQIEA) (Abs da Cruz et al 2004Cruz et al , 2006Sailesh Babu et al 2008). Inspired by the concepts of quantum computing, such as qubits and quantum gates, a QIEA has the following main characteristics.…”

Section: Introductionmentioning

confidence: 98%

Quantum-inspired evolutionary algorithms: a survey and empirical study

2010

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Quantum-inspired evolutionary algorithms, one of the three main research areas related to the complex interaction between quantum computing and evolutionary algorithms, are receiving renewed attention. A quantum-inspired evolutionary algorithm is a new evolutionary algorithm for a classical computer rather than for quantum mechanical hardware. This paper provides a unified framework and a comprehensive survey of recent work in this rapidly growing field. After introducing of the main concepts behind quantum-inspired evolutionary algorithms, we present the key ideas related to the multitude of quantum-inspired evolutionary algorithms, sketch the differences between them, survey theoretical developments and applications that range from combinatorial optimizations to numerical optimizations, and compare the advantages and limitations of these various methods. Finally, a small comparative study is conducted to evaluate the performances of different types of quantum-inspired evolutionary algorithms and conclusions are drawn about some of the most promising future research developments in this area.

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Section: Introductionmentioning

confidence: 98%

Quantum-inspired evolutionary algorithms: a survey and empirical study

2010

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“…This probability presentation has a better characteristic of diversity than classical approaches. QEA have been reported to successfully solve complex benchmark problems such as numerical (da Cruz et al, 2006), multiobjective optimization (Talbi et al, 2006) and real world problems (Jang et al, 2004). The quantum computation also has been extended to PSO and this is known as Quantuminspired Particle Swarm Optimization (QiPSO) (Sun et al, 2004).…”

Section: Quantum Inspired Probability Concept For Feature Selectionmentioning

confidence: 99%

Quantum-Inspired Particle Swarm Optimization for Feature Selection and Parameter Optimization in Evolving Spiking Neural Networks for Classification Tasks

Hamed¹,

Kasabov²,

Shamsuddin³

2011

Evolutionary Algorithms

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“…The methodology was recently extended to include real-valued vectors by da Cruz et al [4]. As for binary-representation QIGA, real-valued QIGA maintains a distinction between a quantum population and an observed population of (in this case) real-valued solution vectors.…”

Section: Real-valued Quantum-inspired Evolutionary Algorithmsmentioning

confidence: 99%

“…This step could be operationalised in a variety of ways with [4] choosing to adopt a variant of uniform crossover, without an explicit selection operator. After the K crossover operations have been performed, with the resulting children being copied into E(t), the best K individuals ∈ C(t) ∪ E(t) are copied into C(t).…”

Section: Crossover Mechanismmentioning

confidence: 99%

Option pricing model calibration using a real-valued quantum-inspired evolutionary algorithm

Fan

Brabazon

O’Sullivan

et al. 2007

Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation

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Quantum effects are a natural phenomenon and just like evolution, or immune processes, can serve as an inspiration for the design of computing algorithms. This study illustrates how a real-valued quantum-inspired evolutionary algorithm (QEA) can be constructed and examines the utility of the resulting algorithm on an important real-world problem, namely the calibration of an Option Pricing model. The results from the algorithm are shown to be robust and sensitivity analysis is carried out on the algorithm parameters, suggesting that there is useful potential to apply QEA to this domain.

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Quantum-Inspired Evolutionary Algorithm for Numerical Optimization

Cited by 43 publications

References 10 publications

Quantum-inspired evolutionary algorithms: a survey and empirical study

Quantum-inspired evolutionary algorithms: a survey and empirical study

Quantum-Inspired Particle Swarm Optimization for Feature Selection and Parameter Optimization in Evolving Spiking Neural Networks for Classification Tasks

Option pricing model calibration using a real-valued quantum-inspired evolutionary algorithm

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