Optimization of the Choquet Integral Using Genetic Algorithm

2014 ◽  
pp. 97-109 ◽  
Author(s):  
Tanja Magoč ◽  
François Modave
Keyword(s):  
Genetic Algorithm ◽  
Choquet Integral

Forecasting stock market price by using fuzzified Choquet integral based fuzzy measures with genetic algorithm for parameter optimization

2020 ◽  
Vol 54 (2) ◽  
pp. 597-614
Author(s):  
Shanoli Samui Pal ◽  
Samarjit Kar
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Linear Regression ◽  
Regression Model ◽  
Choquet Integral ◽  
Time Series Forecasting ◽  
Fuzzy Measure ◽  
Model Parameters ◽  
Forecasting Models ◽  
Non Linear

In this paper, fuzzified Choquet integral and fuzzy-valued integrand with respect to separate measures like fuzzy measure, signed fuzzy measure and intuitionistic fuzzy measure are used to develop regression model for forecasting. Fuzzified Choquet integral is used to build a regression model for forecasting time series with multiple attributes as predictor attributes. Linear regression based forecasting models are suffering from low accuracy and unable to approximate the non-linearity in time series. Whereas Choquet integral can be used as a general non-linear regression model with respect to non classical measures. In the Choquet integral based regression model parameters are optimized by using a real coded genetic algorithm (GA). In these forecasting models, fuzzified integrands denote the participation of an individual attribute or a group of attributes to predict the current situation. Here, more generalized Choquet integral, i.e., fuzzified Choquet integral is used in case of non-linear time series forecasting models. Three different real stock exchange data are used to predict the time series forecasting model. It is observed that the accuracy of prediction models highly depends on the non-linearity of the time series.

scholarly journals A Novel Nonadditive Collaborative-Filtering Approach Using Multicriteria Ratings

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yi-Chung Hu
Keyword(s):  
Genetic Algorithm ◽  
Collaborative Filtering ◽  
Average Method ◽  
Choquet Integral ◽  
Weighted Average ◽  
Multicriteria Decision Making ◽  
Rating Systems ◽  
Group Buying ◽  
Weighted Average Method ◽  
Filtering Approach

Although single-criterion recommender systems have been successfully used in several applications, multicriteria rating systems which allow users to specify ratings for various content attributes of individual items are gaining importance in recommendation context. An overall rating of an unrated item is often obtained by the weighted average method (WAM) when criterion weights are available. However, the assumption of additivity for the WAM is not always reasonable. For this reason, this paper presents a new collaborative-filtering approach using multicriteria ratings, in which a nonadditive technique in Multicriteria decision making (MCDM), namely, the Choquet integral, is used to aggregate multicriteria ratings for unrated items. Subsequently, the system can recommend items with higher overall ratings for each user. The degrees of importance of the respective criteria are determined by a genetic algorithm. In contrast to the additive weighted average aggregation, the Choquet integral does not ignore the interaction among criteria. The applicability of the proposed approach to the recommendation of the initiators on a group-buying website is examined. Experimental results demonstrate that the generalization ability of the proposed approach performs well compared with other similarity-based collaborative-filtering approaches using multicriteria ratings.

The ground state energy of the ±J spin glass from the genetic algorithm

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan
Keyword(s):  
Genetic Algorithm ◽  
Spin Glass ◽  
Ground State Energy ◽  
Ground State ◽  
State Energy

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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