Post hoc analysis of sport performance with differential evolution

Fister, Iztok; Fister, Dušan; Deb, Suash; Mlakar, Uroš; Brest, Janez

doi:10.1007/s00521-018-3395-3

Cited by 24 publications

(19 citation statements)

References 12 publications

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“…Although it is difficult to eliminate all uncertainty from their performance, technical staff and research groups have sought to analyze and identify variables influencing this outcome. Thus, it is unsurprising that there are innumerable studies of physical preparation, technical and tactical aspects, or psychological influences (Fister et al, 2018; Dalen et al, 2019; Henriksen et al, 2019). Among these studies, brain analysis has attracted intense attention in recent years, becoming a prolific field of study and application that will probably continue in future as technical resources are perfected.…”

Section: Physical Activity Sports and The Brainmentioning

confidence: 99%

Physical Activity, Sports Practice, and Cognitive Functioning: The Current Research Status

et al. 2019

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The evidence for the benefits of physical activity on cognitive functioning has increased in recent years. Although the relationship between these variables has been analyzed for decades, the development of evaluation techniques has resolved several issues and advanced this area of knowledge. Moreover, several authors have pointed out the association between the cognitive functioning of athletes and their performance in competition. These recent studies suggest that some specific cognitive abilities of athletes could help them become more effective and improve their chances of success. The objective of this paper was to identify the most relevant advances in these areas of study and to highlight more promising lines of research for the next few years. We have discussed findings from the application of different physical activity programs as well as the most significant cognitive performance variables for sports practice. The limitations of the findings were also discussed.

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Section: Physical Activity Sports and The Brainmentioning

confidence: 99%

Physical Activity, Sports Practice, and Cognitive Functioning: The Current Research Status

et al. 2019

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“…Paper Dataset [27] - [23] Moving images of 15 male college tennis players. [33] Data of Laguna Poreč half-marathon (2017) and Ormož half-marathon (2017). [31] A set of sports performance data of college students.…”

Section: [40]mentioning

confidence: 99%

“…[23] C4.5, Random Forest and Native Bayes Tree. [33] Differential Evolution. [31] k-means, Ant Colony Optimization.…”

Section: [40]mentioning

confidence: 99%

“…[23] Analysis of sports skills data with temporal series image data retrieved from films focused on table tennis. [33] Guide the athletes on how to improve their performance and how to eliminate errors related to the selection of the proper running strategy through the differential evolution algorithm. [31] Proposes a new clustering algorithm based on ant colony optimization.…”

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confidence: 99%

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Computational Intelligence in Sports: A Systematic Literature Review

Bonidia

Rodrigues

Avila-Santos

et al. 2018

Advances in Human-Computer Interaction

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Recently, data mining studies are being successfully conducted to estimate several parameters in a variety of domains. Data mining techniques have attracted the attention of the information industry and society as a whole, due to a large amount of data and the imminent need to turn it into useful knowledge. However, the effective use of data in some areas is still under development, as is the case in sports, which in recent years, has presented a slight growth; consequently, many sports organizations have begun to see that there is a wealth of unexplored knowledge in the data extracted by them. Therefore, this article presents a systematic review of sports data mining. Regarding years 2010 to 2018, 31 types of research were found in this topic. Based on these studies, we present the current panorama, themes, the database used, proposals, algorithms, and research opportunities. Our findings provide a better understanding of the sports data mining potentials, besides motivating the scientific community to explore this timely and interesting topic.

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“…In order to explore this search space effectively, we already have proposed a Differential Evolution (DE) algorithm [2,4] that, in contrast to all previous methods, follows only one attractor. The DE algorithm was selected because of its simplicity and efficiency, and because it was successfully used in various optimization problems [6], such as an animated trees reconstruction [36], an post hoc analysis of sport performance [8], and parametric design and optimization of magnetic gears [33]. The temporal locality [35], selfadaptive mechanism [5] of the main control parameters, local search, and component reinitialization were used additionally to improve the efficiency of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Differential evolution for protein folding optimization based on a three-dimensional AB off-lattice model

Bošković

Brest

2016

J Mol Model

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This paper presents a differential evolution algorithm that is adapted for the protein folding optimization on a three-dimensional AB off-lattice model. The proposed algorithm is based on a self-adaptive differential evolution that improves the algorithm efficiency and reduces the number of control parameters. A mutation strategy for the fast convergence is used inside the algorithm. A temporal locality is used in order to speed up the algorithm convergence additionally and to find amino-acid conformations with the lowest free energy values. Within this mechanism a new vector is calculated when the trial vector is better than the corresponding vector from the population. This new vector is likely better than the trial vector and this accelerates convergence speed. Because of the fast convergence the algorithm has some chance to be trapped into the local optima. To mitigate this problem the algorithm includes reinitialization. The proposed algorithm was tested on amino-acid sequences that are used frequently in literature. The obtained results show that the proposed algorithm is superior to the algorithms from the literature and the obtained amino-acid sequences have significantly lower free energy values. Graphical Abstract Protein folding optimization on a three-dimensional AB off-lattice model using the differential evolution algorithm.

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Post hoc analysis of sport performance with differential evolution

Cited by 24 publications

References 12 publications

Physical Activity, Sports Practice, and Cognitive Functioning: The Current Research Status

Physical Activity, Sports Practice, and Cognitive Functioning: The Current Research Status

Computational Intelligence in Sports: A Systematic Literature Review

Differential evolution for protein folding optimization based on a three-dimensional AB off-lattice model

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