Hybrid fuzzy-genetic algorithm to automated discovery of prediction rules

Fadel, Ibrahim A.; Al-Sanabani, Hussein; Öz, Cemil; Kamal, Tariq; İskefiyeli, Murat; Abdien, Fawzia

doi:10.3233/jifs-182729

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…Experiments show that this comprehensive evaluation method can well improve the utilization efficiency in the process of the comprehensive evaluation of regional tourism resources and has the advantages of fast comprehensive evaluation speed and obvious effect [ 13 ]. Fadel et al conducted comprehensive evaluation from the aspects of selection of evaluation form, classification of evaluation content, and regional management ability and conducted experiments in different regions [ 14 ]. Xu et al put forward a new comprehensive evaluation method of regional tourism resources based on multirelationship recommendation algorithm according to the multifactor relationship theory in collaboration, analyzed the relationship degree of different modules in the traditional comprehensive evaluation of regional tourism resources, and established a multifactor coupling analysis model [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

Comprehensive Evaluation of Tourism Resources Based on Multispecies Evolutionary Genetic Algorithm-Enabled Neural Networks

Kan

Li²

2021

Computational Intelligence and Neuroscience

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With the development of neural network technology and the rapid growth of China’s tourism economic income at this stage, the research on the comprehensive evaluation of tourism resources has gradually emerged. Based on this, this paper studies the neural network comprehensive evaluation model based on multispecies evolutionary genetic algorithm and designs the neural network analysis system of influencing factors of tourism resources based on multispecies evolutionary genetic algorithm. The collection and acquisition of data information are realized from the aspects of resource income status, tourism development investment, and sustainability evaluation in the tourism area. The multispecies evolutionary genetic algorithm is used for comprehensive analysis and evaluation. The algorithm can realize the complex analysis and comprehensive evaluation of the core influencing factors of neural network. Accurate analysis and evaluation were carried out according to the different characteristics of tourism resources and the current situation of tourism income. The results show that the neural network comprehensive evaluation model based on multispecies evolutionary genetic algorithm has the advantages of high practicability, good sorting effect of variable ratio, and good data integration. It can effectively analyze and compare the comprehensive evaluation factors affecting tourism resources in different ratios.

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Section: Related Workmentioning

confidence: 99%

Comprehensive Evaluation of Tourism Resources Based on Multispecies Evolutionary Genetic Algorithm-Enabled Neural Networks

Kan

Li²

2021

Computational Intelligence and Neuroscience

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“…In this paper, we select optimal preservation method, which can ensure that the optimal individuals will not be lost during crossover and mutation. Setting floating crossover probability and variation probability can avoid the result falling into local minima, that is, most of the offspring evolve from same parents (Fadel et al , 2021).…”

Section: Intelligent Obstacle Avoidance Methods For Path Planning Int...mentioning

confidence: 99%

Intelligent obstacle avoidance path planning method for picking manipulator combined with artificial potential field method

Fang

Liang

2022

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Purpose The results of obstacle avoidance path planning for the manipulator using artificial potential field (APF) method contain a large number of path nodes, which reduce the efficiency of manipulators. This paper aims to propose a new intelligent obstacle avoidance path planning method for picking robot to improve the efficiency of manipulators. Design/methodology/approach To improve the efficiency of the robot, this paper proposes a new intelligent obstacle avoidance path planning method for picking robot. In this method, we present a snake-tongue algorithm based on slope-type potential field and combine the snake-tongue algorithm with genetic algorithm (GA) and reinforcement learning (RL) to reduce the path length and the number of path nodes in the path planning results. Findings Simulation experiments were conducted with tomato string picking manipulator. The results showed that the path length is reduced from 4.1 to 2.979 m, the number of nodes is reduced from 31 to 3 and the working time of the robot is reduced from 87.35 to 37.12 s, after APF method combined with GA and RL. Originality/value This paper proposes a new improved method of APF, and combines it with GA and RL. The experimental results show that the new intelligent obstacle avoidance path planning method proposed in this paper is beneficial to improve the efficiency of the robotic arm. Graphical abstract Figure 1 According to principles of bionics, we propose a new path search method, snake-tongue algorithm, based on a slope-type potential field. At the same time, we use genetic algorithm to strengthen the ability of the artificial potential field method for path searching, so that it can complete the path searching in a variety of complex obstacle distribution situations with shorter path searching results. Reinforcement learning is used to reduce the number of path nodes, which is good for improving the efficiency of robot work. The use of genetic algorithm and reinforcement learning lays the foundation for intelligent control.

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“…The core of the genetic algorithm [23][24][25] (GA) is parameter encoding, population initialization, and determination of fitness function, and then the optimal solution is obtained by the search. The classical CNN learning method uses the fastest descent algorithm for learning, and the learning performance is strongly affected by the initial weight settings of the convolutional and fully connected layers.…”

Section: Genetic Algorithm To Optimize Cnn Parametersmentioning

confidence: 99%

A Short Text Classification Model for Electrical Equipment Defects Based on Contextual Features

Zeng

Huang

et al. 2022

Wuhan Univ. J. Nat. Sci.

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The defective information of substation equipment is usually recorded in the form of text. Due to the irregular spoken expressions of equipment inspectors, the defect information lacks sufficient contextual information and becomes more ambiguous. To solve the problem of sparse data deficient of semantic features in classification process, a short text classification model for defects in electrical equipment that fuses contextual features is proposed. The model uses bi-directional long-short term memory in short text classification to obtain the contextual semantics of short text data. Also, the attention mechanism is introduced to assign weights to different information in the context. Meanwhile, this model optimizes the convolutional neural network parameters with the help of the genetic algorithm for extracting salient features. According to the experimental results, the model can effectively realize the classification of power equipment defect text. In addition, the model was tested on an automotive parts repair dataset provided by the project partners, thus enabling the effective application of the method in specific industrial scenarios.

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Hybrid fuzzy-genetic algorithm to automated discovery of prediction rules

Cited by 7 publications

References 13 publications

Comprehensive Evaluation of Tourism Resources Based on Multispecies Evolutionary Genetic Algorithm-Enabled Neural Networks

Comprehensive Evaluation of Tourism Resources Based on Multispecies Evolutionary Genetic Algorithm-Enabled Neural Networks

Intelligent obstacle avoidance path planning method for picking manipulator combined with artificial potential field method

A Short Text Classification Model for Electrical Equipment Defects Based on Contextual Features

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