A Fast Model Based on Genetic Algorithm to Construct Fuzzy Rules

Fuzzy rule has been used extensively in data mining. This paper presents a fast and flexible method based on genetic algorithm to construct fuzzy decision rule with considering criteria of accuracy. First, the algorithm determines the width that divides each attribute into “n” intervals according to the number of fuzzy sets, after that calculates the parameters width according to that width. Rough Sets Model Based on Database Systems technique used to reduce the number of attributes if there exists then we use the algorithm for extracting initial fuzzy rules from fuzzy table using SQL statements with a smaller number of rules than the other models without needing to use a genetic algorithm – Based Rule Selection approach to select a small number of significant rules, then it calculates their accuracy and the confidence.. Multiobjective evolutionary algorithms (EAs) that use nondominated sorting and sharing have been criticized mainly for computational complexity and needing for specifying a sharing parameter but in our genetic model each fuzzy set represented by “Real number” from 0 to 9 forming a gene on chromosome (individual). Our genetic model is used to improve the accuracy of the initial rules and calculates the accuracy of the new rules again which be higher than the old rules The proposed approach is applied on the Iris dataset and the results compared with other models: Preselection with niches, ENORA and NSGA to show its validity.

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An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization

Complexity ◽

10.1155/2018/3051854 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 7

Author(s):

Jing Xiao ◽

Jing-Jing Li ◽

Xi-Xi Hong ◽

Min-Mei Huang ◽

Xiao-Min Hu ◽

...

Keyword(s):

Genetic Algorithm ◽

Evolutionary Algorithms ◽

Portfolio Optimization ◽

Optimization Problem ◽

Experimental Comparison ◽

Software Project ◽

Project Portfolio ◽

Multiobjective Evolutionary Algorithms ◽

Reference Distance ◽

Nondominated Sorting

As it is becoming extremely competitive in software industry, large software companies have to select their project portfolio to gain maximum return with limited resources under many constraints. Project portfolio optimization using multiobjective evolutionary algorithms is promising because they can provide solutions on the Pareto-optimal front that are difficult to be obtained by manual approaches. In this paper, we propose an improved MOEA/D (multiobjective evolutionary algorithm based on decomposition) based on reference distance (MOEA/D_RD) to solve the software project portfolio optimization problems with optimizing 2, 3, and 4 objectives. MOEA/D_RD replaces solutions based on reference distance during evolution process. Experimental comparison and analysis are performed among MOEA/D_RD and several state-of-the-art multiobjective evolutionary algorithms, that is, MOEA/D, nondominated sorting genetic algorithm II (NSGA2), and nondominated sorting genetic algorithm III (NSGA3). The results show that MOEA/D_RD and NSGA2 can solve the software project portfolio optimization problem more effectively. For 4-objective optimization problem, MOEA/D_RD is the most efficient algorithm compared with MOEA/D, NSGA2, and NSGA3 in terms of coverage, distribution, and stability of solutions.

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Studying of portfolio investment fuzzy decision model based on genetic algorithm

IEEE International Conference Mechatronics and Automation, 2005 ◽

10.1109/icma.2005.1626668 ◽

2006 ◽

Author(s):

Xia Liu ◽

Hongxia Chu ◽

Kejun Wang

Keyword(s):

Genetic Algorithm ◽

Decision Model ◽

Portfolio Investment ◽

Fuzzy Decision ◽

Model Based ◽

Fuzzy Decision Model

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Automated classification of stages of anaesthesia by populations of evolutionary optimized fuzzy rules

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2015-0020 ◽

2015 ◽

Vol 1 (1) ◽

pp. 77-79

Author(s):

C. Walther ◽

A. Wenzel ◽

M. Schneider ◽

M. Trommer ◽

K.-P. Sturm ◽

...

Keyword(s):

Evolutionary Algorithms ◽

Embedded System ◽

Vital Signs ◽

Fuzzy Rule ◽

Fuzzy Rules ◽

Classification Model ◽

Multiobjective Evolutionary Algorithms ◽

Automated Classification ◽

Rule Sets

AbstractThe detection of stages of anaesthesia is mainly performed on evaluating the vital signs of the patient. In addition the frontal one-channel electroencephalogram can be evaluated to increase the correct detection of stages of anaesthesia. As a classification model fuzzy rules are used. These rules are able to classify the stages of anaesthesia automatically and were optimized by multiobjective evolutionary algorithms. As a result the performance of the generated population of fuzzy rule sets is presented. A concept of the construction of an autonomic embedded system is introduced. This system should use the generated rules to classify the stages of anaesthesia using the frontal one-channel electroencephalogram only.

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Design Of Fuzzy Rule-Based System Using Genetic Algorithms

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst/21/11938 ◽

2021 ◽

Vol 23 (11) ◽

pp. 683-692

Author(s):

Sanjay Charaya ◽

◽

Kapil Mehta ◽

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Fuzzy Rule ◽

Fixed Number ◽

Fuzzy Rules ◽

Observation Data ◽

Dimensional Solution ◽

Air Conditioning System ◽

Rule Based ◽

Genetic Algorithm Approach

The aim of this paper is to obtain a compact and optimal fuzzy rule-based model from observation data by utilizing the Genetic algorithm technique. The approach is optimized by applying Genetic Algorithms, owing to its capability of searching irregular and high dimensional solution spaces. Genetic Algorithms has been applied to learn consequent part of fuzzy rules and models with fixed number of rules. In the work we propose a Genetic algorithm approach to a non-linear air conditioning system for the construction of optimal fuzzy rules in two steps. First, fuzzy clustering is applied to obtain an initial rule based model having pre-calculated number of rules with antecedents only. In the second step, the regions of rule-consequents are obtained by a binary coded Genetic Algorithm which leads to the extraction of an optimal rule based model.

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Integrity Evaluation of Customs Cooperation Based on Gray and Fuzzy Decision Theory

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.6-7.711 ◽

2012 ◽

Vol 6-7 ◽

pp. 711-716

Author(s):

Cai Hong Hou ◽

Hong Xia Li ◽

Bing Zhi Huang ◽

Yi Gong

Keyword(s):

Genetic Algorithm ◽

Decision Theory ◽

Quantitative Evaluation ◽

Evaluation System ◽

Index Structure ◽

Fuzzy Decision ◽

Model Based ◽

Gray Theory ◽

Hierarchy Analysis ◽

Enterprise Behavior

The integrity evaluation of cooperation does well to standardizing enterprise behavior, and constructing of an orderly competitive operating background. Evaluation term and method are most important in evaluation. In this paper, an evaluation system was designed, which included 3-level evaluation terms and a fusing optimized algorithm. During the course, multi-hierarchy analysis was used to design index structure firstly, and then the integrated Gray theory and Genetic algorithm were introduced to optimize index’s weight. The innovation was reflected in article included an evaluation system with customs characteristics, and cooperation’s integrity graded model based on quantitative evaluation.

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