Hybrid fuzzy-genetic algorithm to automated discovery of prediction rules

Genetic algorithm is one of data mining classification techniques and it has been applied successfully in a wide range of applications. However, the performance of Genetic algorithm fluctuates significantly. This research work combines Genetic algorithm with fuzzy logic to adapt dynamically crossover and mutation parameters of Genetic algorithm. Two different datasets are taken during the experiment. Several experiments have been performed to prove the effectiveness of the proposed algorithm. Results show that the rules generated from a proposed algorithm are significantly better with high fitness and more efficient as compared to a normal Genetic algorithm.

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Cost-Optimization Problem with a Soft Time Window Based on an Improved Fuzzy Genetic Algorithm for Fresh Food Distribution

Mathematical Problems in Engineering ◽

10.1155/2018/5743287 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 2

Author(s):

Yong Ming Wang ◽

Hong Li Yin

Keyword(s):

Genetic Algorithm ◽

Cost Model ◽

Research Work ◽

Food Distribution ◽

Food Plants ◽

Total Cost ◽

Fresh Food ◽

Fuzzy Clustering Method ◽

Fuzzy Genetic Algorithm ◽

Fuzzy Genetic

Given the increasing demand for fresh food quality, fresh food plants must manage not only product cost but more importantly the product quality. The transportation requirements for fresh food delivery have been continuously increasing. The purpose of this paper is to develop a method to ensure that fresh food can be delivered just in time and with minimum total cost while maintaining the quality of fresh food. Considering that fresh food plants need multiple trucks to deliver multiple products to numerous geographically dispersed customers, the delivery of fresh food is considered in two stages in our study. The first stage is cluster consumers; that is, we determine to which consumers each truck is responsible for delivery. The second stage, which is based on the consumer grouping results, develops a total cost model that includes the transportation, refrigerated, devalued, and penalty costs incurred during distribution. This model is used to determine the optimal route selection, the temperature control, and the average speed of each truck in distribution. This paper designs decision variables based on a customer’s seven requirement attributes; it also proposes a fuzzy clustering method for grouping customers and improves a fuzzy genetic algorithm that is used to solve the proposed total cost model. The application of the proposed method is demonstrated using an example. The experimental results show that the proposed method has better performance than that of a traditional genetic algorithm. This research work provides an optimal distribution total cost decision method for the logistics managers. This research also provides an effective means to ensure the safety of fresh food.

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Construction of Student Information Management System Based on Data Mining and Clustering Algorithm

Complexity ◽

10.1155/2021/4447045 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

XueHong Yin

Keyword(s):

Data Mining ◽

Genetic Algorithm ◽

Information Management ◽

Management System ◽

Clustering Algorithm ◽

Student Evaluation ◽

Information Management System ◽

Fuzzy Genetic Algorithm ◽

Student Information ◽

Fuzzy Genetic

Data mining is a new technology developed in recent years. Through data mining, people can discover the valuable and potential knowledge hidden behind the data and provide strong support for scientifically making various business decisions. This paper applies data mining technology to the college student information management system, mines student evaluation information data, uses data mining technology to design student evaluation information modules, and digs out the factors that affect student development and the various relationships between these factors. Predictive assessment of knowledge and personalized teaching decision-making provide the basis. First, the general situation of genetic algorithm and fuzzy genetic algorithm is introduced, and then, an improved genetic fuzzy clustering algorithm is proposed. Compared with traditional clustering algorithm and improved genetic fuzzy clustering algorithm, the effectiveness of the algorithm proposed in this paper is proved. Based on the analysis system development related tools and methods, in response to the needs of the student information management system, a simple student information management system is designed and implemented, which provides a platform and data source for the next application of clustering algorithm for performance analysis. Finally, clustering the students’ scores with a clustering algorithm based on fuzzy genetic algorithm, the experimental results show that this method can better analyze the students’ scores and help relevant teachers and departments make decisions.

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