Parallel memetic structures

Caraffini, Fabio; Neri, Ferrante; Iacca, Giovanni; Mól, Antônio Carlos de Abreu

doi:10.1016/j.ins.2012.11.017

Cited by 99 publications

(56 citation statements)

References 48 publications

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“…In particular, a coordination of multiple local search components is used to tackle LSOPs in (23). This logic, a part or a modification of it has been coupled and integrated within other algorithmic frameworks in (24), (25), (26), (27), and (28). It must be remarked that these algorithms tend to use a simple local search component that exploits the decision space by perturbing the candidate solution along the axes.…”

Section: Introductionmentioning

confidence: 99%

Large Scale Problems in Practice: The Effect of Dimensionality on the Interaction Among Variables

Caraffini

Neri

Iacca

2017

Lecture Notes in Computer Science

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Abstract. This article performs a study on correlation between pairs of variables in dependence on the problem dimensionality. Two tests, based on Pearson and Spearman coefficients, have been designed and used in this work. In total, 86 test problems ranging between 10 and 1000 variables have been studied. If the most commonly used experimental conditions are used, the correlation between pairs of variables appears, from the perspective of the search algorithm, to consistently decrease. This effect is not due to the fact that the dimensionality modifies the nature of the problem but is a consequence of the experimental conditions: the computational feasibility of the experiments imposes an extremely shallow search in case of high dimensions. An exponential increase of budget and population with the dimensionality is still practically impossible. Nonetheless, since real-world application may require that large scale problems are tackled despite of the limited budget, an algorithm can quickly improve upon initial guesses if it integrates the knowledge that an apparent weak correlation between pairs of variables occurs, regardless the nature of the problem.

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Section: Introductionmentioning

confidence: 99%

Large Scale Problems in Practice: The Effect of Dimensionality on the Interaction Among Variables

Caraffini

Neri

Iacca

2017

Lecture Notes in Computer Science

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“…It should also be noted that the DE/rand/1 has been proved to be efficient to improve the algorithm's performances on complicated MOPs, it helps the MOEA/D winning unconstrained multi-objective evolutionary algorithm competition in the congress of evolutionary computation (CEC 2009) [50] . Different from the above two operators, the LocalSearch1 in MTS for large scale global optimization searches along each axis of the decision space one by one rather than along the current PS [51][52][53] . This operator is a short distance exploration operator that can be seen as a modification of hill-descend search .…”

Section: Adaptive Genetic Operator Selection In Moea/dmentioning

confidence: 99%

Self-adaptive multi-objective evolutionary algorithm based on decomposition for large-scale problems: A case study on reservoir flood control operation

et al. 2016

Information Sciences

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“…In this paper, the initial local search is performed by the so-called Short Distance Exploration or simply S algorithm, see [30], [31], and [32]. The S algorithm is a simple greedy local search that performs moves along the axes and halves its radius when it is unable to detect a better solution.…”

Section: B Super-fit Dementioning

confidence: 99%

Differential evolution schemes for speech segmentation: A comparative study

Iliya

Neri

Menzies

et al. 2014

2014 IEEE Symposium on Differential Evolution (SDE)

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Abstract-This paper presents a signal processing technique for segmenting short speech utterances into unvoiced and voiced sections and identifying points where the spectrum becomes steady. The segmentation process is part of a system for deriving musculoskeletal articulation data from disordered utterances, in order to provide training feedback. The functioning of the signal processing technique has been optimized by selecting the parameters of the model. The optimization has been carried out by testing and comparing multiple Differential Evolution implementations, including a standard one, a memetic one, and a controlled randomized one. Numerical results have also been compared with a famous and efficient swarm intelligence algorithm. For the given problem, Differential Evolution schemes appear to display a very good performance as they can quickly reach a high quality solution. The binomial crossover appears, for the given problem, beneficial with respect to the exponential one. The controlled randomization appears to be the best choice in this case. The overall optimized system proved to segment well the speech utterances and efficiently detect its uninteresting parts.

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Parallel memetic structures

Cited by 99 publications

References 48 publications

Large Scale Problems in Practice: The Effect of Dimensionality on the Interaction Among Variables

Large Scale Problems in Practice: The Effect of Dimensionality on the Interaction Among Variables

Self-adaptive multi-objective evolutionary algorithm based on decomposition for large-scale problems: A case study on reservoir flood control operation

Differential evolution schemes for speech segmentation: A comparative study

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