An adaptive genetic algorithm for solving bilevel linear programming problem

2007 ◽  
Vol 28 (12) ◽  
pp. 1605-1612 ◽  
Author(s):  
Guang-min Wang ◽  
Xian-jia Wang ◽  
Zhong-ping Wan ◽  
Shi-hui Jia
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Programming Problem ◽  
Linear Programming Problem ◽  
Adaptive Genetic Algorithm ◽  
Bilevel Linear Programming

scholarly journals Solving the Fully Fuzzy Bilevel Linear Programming Problem through Deviation Degree Measures and a Ranking Function Method

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Aihong Ren
Keyword(s):  
Linear Programming ◽  
Programming Problem ◽  
Linear Programming Problem ◽  
Function Method ◽  
Optimal Solution ◽  
Ranking Function ◽  
Objective Functions ◽  
Bilevel Linear Programming ◽  
Fuzzy Optimal Solution ◽  
Deviation Degree

This paper is concerned with a class of fully fuzzy bilevel linear programming problems where all the coefficients and decision variables of both objective functions and the constraints are fuzzy numbers. A new approach based on deviation degree measures and a ranking function method is proposed to solve these problems. We first introduce concepts of the feasible region and the fuzzy optimal solution of a fully fuzzy bilevel linear programming problem. In order to obtain a fuzzy optimal solution of the problem, we apply deviation degree measures to deal with the fuzzy constraints and use a ranking function method of fuzzy numbers to rank the upper and lower level fuzzy objective functions. Then the fully fuzzy bilevel linear programming problem can be transformed into a deterministic bilevel programming problem. Considering the overall balance between improving objective function values and decreasing allowed deviation degrees, the computational procedure for finding a fuzzy optimal solution is proposed. Finally, a numerical example is provided to illustrate the proposed approach. The results indicate that the proposed approach gives a better optimal solution in comparison with the existing method.

Improving Effectiveness of Intrusion Detection by Correlation Feature Selection

2012 ◽  
pp. 22-35
Author(s):  
Hai Thanh Nguyen ◽  
Katrin Franke ◽  
Slobodan Petrovic
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Linear Programming ◽  
Intrusion Detection ◽  
Programming Problem ◽  
Linear Programming Problem ◽  
Selection Procedure ◽  
Filter Method ◽  
New Feature ◽  
Kdd Cup 99

In this paper, the authors propose a new feature selection procedure for intrusion detection, which is based on filter method used in machine learning. They focus on Correlation Feature Selection (CFS) and transform the problem of feature selection by means of CFS measure into a mixed 0-1 linear programming problem with a number of constraints and variables that is linear in the number of full set features. The mixed 0-1 linear programming problem can then be solved by using branch-and-bound algorithm. This feature selection algorithm was compared experimentally with the best-first-CFS and the genetic-algorithm-CFS methods regarding the feature selection capabilities. Classification accuracies obtained after the feature selection by means of the C4.5 and the BayesNet over the KDD CUP’99 dataset were also tested. Experiments show that the authors’ method outperforms the best-first-CFS and the genetic-algorithm-CFS methods by removing much more redundant features while keeping the classification accuracies or getting better performances.

Improving Effectiveness of Intrusion Detection by Correlation Feature Selection

2011 ◽  
Vol 3 (1) ◽  
pp. 21-34 ◽  
Author(s):  
Hai Thanh Nguyen ◽  
Katrin Franke ◽  
Slobodan Petrovic
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Linear Programming ◽  
Intrusion Detection ◽  
Programming Problem ◽  
Linear Programming Problem ◽  
Selection Procedure ◽  
Filter Method ◽  
Selection Algorithm ◽  
New Feature

In this paper, the authors propose a new feature selection procedure for intrusion detection, which is based on filter method used in machine learning. They focus on Correlation Feature Selection (CFS) and transform the problem of feature selection by means of CFS measure into a mixed 0-1 linear programming problem with a number of constraints and variables that is linear in the number of full set features. The mixed 0-1 linear programming problem can then be solved by using branch-and-bound algorithm. This feature selection algorithm was compared experimentally with the best-first-CFS and the genetic-algorithm-CFS methods regarding the feature selection capabilities. Classification accuracies obtained after the feature selection by means of the C4.5 and the BayesNet over the KDD CUP’99 dataset were also tested. Experiments show that the authors’ method outperforms the best-first-CFS and the genetic-algorithm-CFS methods by removing much more redundant features while keeping the classification accuracies or getting better performances.

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