Reward-Penalty Assignments and Genetic Algorithms for Ordinal Interval Number Group Decision Making

2014 ◽  
Vol 29 (8) ◽  
pp. 727-750 ◽  
Author(s):  
Tatiana Tambouratzis ◽  
Vassileios Canellidis
Keyword(s):  
Decision Making ◽  
Genetic Algorithms ◽  
Group Decision Making ◽  
Group Decision ◽  
Interval Number

Case-Based Reasoning Model with Genetic Algorithms, Group Decision-Making and Template Reduction

2016 ◽  
Vol 25 (02) ◽  
pp. 1550032 ◽  
Author(s):  
Aijun Yan ◽  
Hairuo Song ◽  
Pu Wang
Keyword(s):  
Decision Making ◽  
Genetic Algorithms ◽  
Group Decision Making ◽  
Nearest Neighbor ◽  
Group Decision ◽  
Small Samples ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Attribute Weights ◽  
Case Based

Case retrieval, case reuse and case retention are critical to the reasoning performance of the traditional case-based reasoning (CBR) model. In this paper, the integrated use of template reduction technology (TR), genetic algorithms (GA), nearest neighbor (NN) rules and group decision-making (GDM) establishes the CBR-GDM model. First, the TR method of the case base is introduced. Then, an attribute weights optimization using GA is discussed in the case retrieval phase. After that, a case reuse method is carried out with NN and GDM. Finally, 10 data sets from UCI are used to carry out a comparison experiment by 5-fold cross-validation. The classification accuracy rate and the classification efficiency are analyzed under the small samples, before and after the data reduction. The results show that, combined with TR, GA and GDM, the pattern classification performance by CBR can be improved.

Research on group decision making with interval numbers based on plant growth simulation algorithm

Kybernetes ◽  
2014 ◽  
Vol 43 (2) ◽  
pp. 250-264 ◽  
Author(s):  
Lei Li ◽  
Xiaolu Xie ◽  
Rui Guo
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Interval Number ◽  
Simulation Algorithm ◽  
Content Type ◽  
Growth Simulation ◽  
Plant Growth Simulation Algorithm ◽  
Preference Matrix

Purpose – This paper aims at multi-attribute and multi-program group decision making when the attribute weights are completely unknown and the attribute value information is in the form of the interval number. Design/methodology/approach – This is an artificial intelligence algorithm for designing information gathering in group decision making. The authors propose the nonlinear programming model to gather information based on plant growth simulation algorithm (PGSA). The authors collect each program on each attribute group decision preference ordering interval and then use them to find the preference vector and the preference matrix. The entropy method is used to determine the weight of each attribute by the constructed preference matrix. According to the possibility degree matrix of each attribute, the combined effect vector is established by the priority weight vector method, which sorts and selects the best decision making program. Findings – To the authors' knowledge, the application of PGSA in the field of management decisions to collect program on each attribute group decision making preference interval number is the first trial in literature. It has retained more valuable decision making information from all experts without distortion. Practical implications – In practice, a real number may not be an accurate representation, but only gives a range of values to describe the attributes. This study provides a useful measurement of interval number information for managers to evaluate military science, venture capital, and environmental assessment, etc. Originality/value – The methodology considers the complete information to ensure no information distortion even with large and complex systems. The authors adopt computer artificial intelligence algorithms to obtain the objective evaluation, which is meaningful for both research studies and practical use.

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