A novel binary chaotic genetic algorithm for feature selection and its utility in affective computing and healthcare

Tahir, Madiha; Tubaishat, Abdallah; Al-Obeidat, Feras; Shah, Babar; Halim, Zahid; Waqas, Muhammad

doi:10.1007/s00521-020-05347-y

Cited by 43 publications

(21 citation statements)

References 39 publications

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“…Chaotic helps the proposed algorithm to alleviate local optima and enhance the convergence. Tahir et al [193] presented a binary chaotic GA for feature selection in healthcare. The chaotic maps were used to initialize the population and modified reproduction operators were applied on population.…”

Section: Chaotic Gasmentioning

confidence: 99%

A review on genetic algorithm: past, present, and future

Katoch

Chauhan

Kumar

2020

Multimed Tools Appl

2,397

761

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In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

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Section: Chaotic Gasmentioning

confidence: 99%

A review on genetic algorithm: past, present, and future

Katoch

Chauhan

Kumar

2020

Multimed Tools Appl

2,397

761

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“…For instance, Martinez et al [21] presented a genetic search-based feature selection method for improving the accuracy of the affective models, comparing it against sequential forward feature selection and random search in a game survey dataset. Tahir et al [37] introduced a binary chaotic genetic algorithm for feature selection, which achieved scores two times higher than a baseline genetic algorithm in identifying seven emotional states. Finally, Alvarez et al [1] employed artificial evolution to select speech feature subsets that optimize the success rate of emotion recognition.…”

Section: Modeling Affect Via Evolutionary Searchmentioning

confidence: 99%

RankNEAT: Outperforming Stochastic Gradient Search in Preference Learning Tasks

Pinitas,

Makantasis,

Liapis

et al. 2022

Preprint

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Stochastic gradient descent (SGD) is a premium optimization method for training neural networks, especially for learning objectively defined labels such as image objects and events. When a neural network is instead faced with subjectively defined labels-such as human demonstrations or annotations-SGD may struggle to explore the deceptive and noisy loss landscapes caused by the inherent bias and subjectivity of humans. While neural networks are often trained via preference learning algorithms in an effort to eliminate such data noise, the de facto training methods rely on gradient descent. Motivated by the lack of empirical studies on the impact of evolutionary search to the training of preference learners, we introduce the RankNEAT algorithm which learns to rank through neuroevolution of augmenting topologies. We test the hypothesis that RankNEAT outperforms traditional gradient-based preference learning within the affective computing domain, in particular predicting annotated player arousal from the game footage of three dissimilar games. RankNEAT yields superior performances compared to the gradient-based preference learner (RankNet) in the majority of experiments since its architecture optimization capacity acts as an efficient feature selection mechanism, thereby, eliminating overfitting. Results suggest that RankNEAT is a viable and highly efficient evolutionary alternative to preference learning. CCS CONCEPTS• Computing methodologies → Neural networks; Genetic algorithms; • Human-centered computing → Human computer interaction (HCI); • Applied computing → Computer games.

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“…GA is a mathematical model that simulates the process of selecting genes in a biological method. GA is based on solutions to mathematical problems that do not contain one fixed and straightforward explanation but consist of a set of solutions [29]. GA is based primarily on Darwin's theory.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

A Novel Urban Tourism Path Planning Approach Based on a Multiobjective Genetic Algorithm

Damos

Zhu

et al. 2021

IJGI

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One of the most important variables that leads to effective individual and group tours is the tourism route planning approach, which enables tourists to engage with tourism with ease, speed, and safety. However, current methods of designing tourist routes have some glitches, such as relying only on external objectives to find the best route. In this paper, a novel urban tourism path planning method based on a multiobjective genetic algorithm is proposed. The main goal of this paper is to enhance the accuracy of the genetic algorithm (GA) by adopting new parameters and selecting the optimal tourism path by combining external and internal tourist site potentials. Moreover, the GA and analytical hierarchy process (AHP) were used in our proposed approach to evaluate urban tourism route planning under multiple conflicting objectives. To visualize and execute the proposed approach, the geographic information system (GIS) environment was used. Our suggested approach has been applied to develop the tourist road network of Chengdu City in China. Compared with existing tourism path planning approaches, our proposed approach is more accurate and straightforward than other approaches used to choose routes.

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A novel binary chaotic genetic algorithm for feature selection and its utility in affective computing and healthcare

Cited by 43 publications

References 39 publications

A review on genetic algorithm: past, present, and future

A review on genetic algorithm: past, present, and future

RankNEAT: Outperforming Stochastic Gradient Search in Preference Learning Tasks

A Novel Urban Tourism Path Planning Approach Based on a Multiobjective Genetic Algorithm

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