scholarly journals Study of Genetic Algorithms Behavior for High Epitasis and High Dimensional Deceptive Functions

10.5772/9622 ◽  
2009 ◽  
Author(s):  
Gras Robin
Keyword(s):  
Genetic Algorithms ◽  
High Dimensional

scholarly journals A Master Slave Parallel Genetic Algorithm for Feature Selection in High Dimensional Datasets

2019 ◽  
Vol 8 (3) ◽  
pp. 379-384
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Feature Selection ◽  
Information Gain ◽  
Optimal Number ◽  
Good Choice ◽  
High Dimensional ◽  
Parallel Genetic Algorithm ◽  
Efficient Manner ◽  
High Dimensional Datasets

Feature Selection in High Dimensional Datasets is a combinatorial problem as it selects the optimal subsets from N dimensional data having 2N possible subsets. Genetic Algorithms are generally a good choice for feature selection in large datasets, though for some high dimensional problems it may take varied amount of time - few seconds, few hours or even few days. Therefore, it is important to use Genetic Algorithms that can give quality results in reasonably acceptable time limit. For this purpose, it is becoming necessary to implement Genetic Algorithms in an efficient manner. In this paper, a Master Slave Parallel Genetic Algorithm is implemented as a Feature Selection procedure to diminish the time intricacies of sequential genetic algorithm. This paper describes the speed gains in parallel Master-Slave Genetic Algorithm and also discusses the theoretical analysis of optimal number of slaves required for an efficient master slave implementation. The experiments are performed on three high-dimensional gene expression data. As Genetic Algorithm is a wrapper technique and takes more time to find the importance of any feature, Information Gain technique is used first as pre-processing task to remove the irrelevant features.

Linguistic Rule Extraction from Numerical Data for High-dimensional Classification Problems

1999 ◽  
Vol 3 (5) ◽  
pp. 386-393
Author(s):  
Hisao Ishibuchi ◽  
◽  
Tadahiko Murata ◽  
Tomoharu Nakashima ◽  
Keyword(s):  
Genetic Algorithms ◽  
Screening Method ◽  
Numerical Data ◽  
Rule Extraction ◽  
High Dimensional ◽  
Classification Problems ◽  
Rule Selection ◽  
Linguistic Rule ◽  
Dimensional Classification ◽  
Linguistic Rules

We discuss the linguistic rule extraction from numerical data for high-dimensional classification problems. Difficulties in the handling of high-dimensional problems stem from the curse of dimensionality: the number of combinations of antecedent linguistic values exponentially increases as the number of attributes increases. Our goal is to extract a small number of simple linguistic rules with high classification ability. In this paper, the rule extraction is to find a set of linguistic rules using three criteria: its classification ability, its compactness, and the simplicity of each rule. Our approach consists of two phases: candidate rule generation and rule selection. We first propose a pre-screening method for generating a tractable number of promising candidate rules for high-dimensional classification problems where it is impossible to examine all combinations of antecedent linguistic values. Next we show how genetic algorithms can be applied to the rule selection. Then we combine a heuristic rule elimination procedure with genetic algorithms for improving their search ability. Finally, the performance of our approach is examined by computer simulations on commonly used data sets.

Crossover Strategy for Improved Solution Space Exploration With Genetic Algorithms

1998 ◽  
Author(s):  
Kurt S. Anderson ◽  
YuHung Hsu
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Single Point ◽  
Solution Space ◽  
Nonuniform Distribution ◽  
High Dimensional ◽  
Optimal Solutions ◽  
Crossover Operator ◽  
Entire Chromosome ◽  
Dimensional Optimization

Abstract The following paper presents a modified crossover operator to extend the exploration capability in Genetic Algorithms for high dimensional optimization problems. Traditional strategies apply crossover once on a pair of selected chromosomes to generate two offspring by randomly selecting a single crossover location within the chromosomal length. The proposed method applies crossover once on each separate gene (variable) instead of on the entire chromosome. To further accelerate exploration of the Genetic Algorithm, nonuniform distribution of the respective crossover position on each gene has also been studied. The empirical results show that Genetic Algorithms with the proposed crossover strategies can find optimal or near optimal solutions within fewer generations than traditional single point crossover.

Sign in / Sign up

Export Citation Format

Share Document