An adaptive memetic algorithm for feature selection using proximity graphs

Zaher, Amer Abu; Berretta, Regina; Noman, Nasimul; Moscato, Pablo

doi:10.1111/coin.12196

Cited by 11 publications

(3 citation statements)

References 36 publications

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“…The main characteristic of this second type of learning is that algorithms do not have access to labels, therefore the problem is no longer to find a map but instead analyze how points are organized in the input space . Application of intelligent computing can be studied Vasant et al, Panda et al, Abu Zaher et al and integration of SA and clustering algorithm is elaborated in Seifollahi …”

Section: Mathematical Formulation and The Methodologymentioning

confidence: 99%

Efficiency analysis for stochastic dynamic facility layout problem using meta‐heuristic, data envelopment analysis and machine learning

Tayal

Köse

Solanki

et al. 2019

Computational Intelligence

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The facility layout problem (FLP) is a combinatorial optimization problem. The performance of the layout design is significantly impacted by diverse, multiple factors. The use of algorithmic or procedural design methodology in ranking and identification of efficient layout is ineffective. In this context, this study proposes a three‐stage methodology where data envelopment analysis (DEA) is augmented with unsupervised and supervised machine learning (ML). In stage 1, unsupervised ML is used for the clustering of the criteria in which the layouts need to be evaluated using homogeneity. Layouts are generated using simulated annealing, chaotic simulated annealing, and hybrid firefly algorithm/chaotic simulated annealing meta‐heuristics. In stage 2, the nonparametric DEA approach is used to identify efficient and inefficient layouts. Finally, supervised ML utilizes the performance frontiers from DEA (efficiency scores) to generate a trained model for getting the unique rankings and predicted efficiency scores of layouts. The proposed methodology overcomes the limitations associated with large datasets that contain many inputs / outputs from the conventional DEA and improves the prediction accuracy of layouts. A Gaussian distribution product demand dataset for time period T = 5 and facility size N = 12 is used to prove the effectiveness of the methodology.

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Section: Mathematical Formulation and The Methodologymentioning

confidence: 99%

Efficiency analysis for stochastic dynamic facility layout problem using meta‐heuristic, data envelopment analysis and machine learning

Tayal

Köse

Solanki

et al. 2019

Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…In 2019, a regression approach based on 'Continued Fraction' (CFR) was proposed; it views multivariate regression as a non-linear optimization problem and the authors used a memetic algorithm to find approximations to the unknown target functions from experimental data [30]. Memetic algorithms are a population-based approach to solve computational problems that are posed as optimization tasks and have been heavily used for other data analytics in combinatorial optimization problems [31,32,33] and that are also showing impressive results for non-linear regression problems [34,30,35,36] and other machine learning problems [37].…”

Section: Continued Fraction Regressionmentioning

confidence: 99%

Multiple regression techniques for modeling dates of first performances of Shakespeare-era plays

Moscato,

Craig,

Egan

et al. 2021

Preprint

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The date of the first performance of a play of Shakespeare's time must usually be guessed with reference to multiple indirect external sources, or to some aspect of the content or style of the play. Identifying these dates is important to literary history and to accounts of developing authorial styles, such as Shakespeare's. In this study, we took a set of Shakespeare-era plays (181 plays from the period 1585-1610), added the best-guess dates for them from a standard reference work as metadata, and calculated a set of probabilities of individual words in these samples. We applied 11 regression methods to predict the dates of the plays at an 80/20 training/test split. We withdrew one play at a time, used the best-guess date metadata with the probabilities and weightings to infer its date, and thus built a model of date-probabilities interaction. We introduced a memetic algorithm-based Continued Fraction Regression (CFR) which delivered models using a small number of variables, leading to an interpretable model and reduced dimensionality. An in-depth analysis of the most commonly occurring 20 words in the CFR models in 100 independent runs helps explain the trends in linguistic and stylistic terms. The analysis with the subset of words revealed an interesting correlation of signature words with the Shakespeare-era play's genre.Keywords Shakespeare-era plays • continued fraction regression • dating of plays • play's genre • Memetic Algorithm

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“…This method can ensure that at the beginning of the algorithm, particles can search for better seeds in the global scope at a higher speed, while in the later stage of the algorithm, the particle can conduct detailed local search, that is, it has better local optimization ability. 11…”

Section: Nonlinear Dynamic Improved the Inertia Weightsmentioning

confidence: 99%

Integrated kitchen design and optimization based on the improved particle swarm intelligent algorithm

2020

Computational Intelligence

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The layout and design of the integrated kitchen can affect the efficiency of people's cooking work greatly. An excellent integrated kitchen design requires each kitchen cabinet module to meet certain constraints and reach the highest work efficiency in a certain space. In this article, we proposed an improved particle swarm intelligence algorithm (IPSO, for short) method by initializing the population chaos, dynamically improving the inertia weight and adjusting the acceleration factor, and applied in the kitchen design and optimization. This method combines the mathematical intelligent algorithm with the integrated kitchen design for the first time, and further selects the optimal design scheme from the preliminary schemes according to the fitness curve of the kitchen mathematical model, which provides the theoretical basis for the refined design of kitchen products. The method can also be used in home design, interior design, and other related areas. K E Y W O R D S design, inertia weight, integrated kitchen, optimization, particle swarm intelligent algorithm How to cite this article: Sun X, Ji X. Integrated kitchen design and optimization based on the improved particle swarm intelligent algorithm.

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An adaptive memetic algorithm for feature selection using proximity graphs

Cited by 11 publications

References 36 publications

Efficiency analysis for stochastic dynamic facility layout problem using meta‐heuristic, data envelopment analysis and machine learning

Efficiency analysis for stochastic dynamic facility layout problem using meta‐heuristic, data envelopment analysis and machine learning

Multiple regression techniques for modeling dates of first performances of Shakespeare-era plays

Integrated kitchen design and optimization based on the improved particle swarm intelligent algorithm

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