Genetic Algorithm for Optimization of Multiple Objectives in Knowledge Discovery from Large Databases

2008 ◽  
pp. 1-22 ◽  
Author(s):  
Satchidananda Dehuri ◽  
Susmita Ghosh ◽  
Ashish Ghosh
Keyword(s):  
Genetic Algorithm ◽  
Knowledge Discovery ◽  
Multiple Objectives ◽  
Large Databases

Discovering Frequent Embedded Subtree Patterns from Large Databases of Unordered Labeled Trees

2008 ◽  
pp. 3235-3251
Author(s):  
Yongqiao Xiao ◽  
Jenq-Foung Yao ◽  
Guizhen Yang
Keyword(s):  
Knowledge Discovery ◽  
Web Sites ◽  
Synthetic Data ◽  
Research Interest ◽  
Data Sets ◽  
Labeled Trees ◽  
Computational Overhead ◽  
Large Databases ◽  
Frequency Counts ◽  
Novel Algorithm

Recent years have witnessed a surge of research interest in knowledge discovery from data domains with complex structures, such as trees and graphs. In this paper, we address the problem of mining maximal frequent embedded subtrees which is motivated by such important applications as mining “hot” spots of Web sites from Web usage logs and discovering significant “deep” structures from tree-like bioinformatic data. One major challenge arises due to the fact that embedded subtrees are no longer ordinary subtrees, but preserve only part of the ancestor-descendant relationships in the original trees. To solve the embedded subtree mining problem, in this article we propose a novel algorithm, called TreeGrow, which is optimized in two important respects. First, it obtains frequency counts of root-to-leaf paths through efficient compression of trees, thereby being able to quickly grow an embedded subtree pattern path by path instead of node by node. Second, candidate subtree generation is highly localized so as to avoid unnecessary computational overhead. Experimental results on benchmark synthetic data sets have shown that our algorithm can outperform unoptimized methods by up to 20 times.

Genetic-algorithm-based optimal apportionment of reliability and redundancy under multiple objectives

2009 ◽  
Vol 41 (4) ◽  
pp. 287-298 ◽  
Author(s):  
Hong-Zhong Huang ◽  
Jian Qu ◽  
Ming J. Zuo
Keyword(s):  
Genetic Algorithm ◽  
Multiple Objectives

Knowledge discovery using genetic algorithm for maritime situational awareness

2014 ◽  
Vol 41 (6) ◽  
pp. 2742-2753 ◽  
Author(s):  
Chun-Hsien Chen ◽  
Li Pheng Khoo ◽  
Yih Tng Chong ◽  
Xiao Feng Yin
Keyword(s):  
Genetic Algorithm ◽  
Knowledge Discovery ◽  
Situational Awareness

Optimization of Vehicle Routing Problem Based on Multi-Objective Genetic Algorithm

2012 ◽  
Vol 253-255 ◽  
pp. 1356-1359
Author(s):  
Ru Zhong ◽  
Jian Ping Wu ◽  
Yi Man Du
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Heuristic Algorithm ◽  
Efficient Method ◽  
Vehicle Routing Problem ◽  
Multiple Objectives ◽  
Routing Problem ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

When there are multiple objectives co-existent in Vehicle routing problem(VRP), it is difficult to achieve optical status simultaneously. To solve this issue, it introduces a method of improved multi-objective Genetic Algorithm (MOGA). It adopts an approach close to heuristic algorithm to cultivate partial viable chromosomes, route decoding to ensure that all individuals meet constraints and uses relatively efficient method of arena contest to construct non-dominated set. Finally programme to fulfill the multi-objective algorithm and then apply it in the standard example of VRP to verity its effectiveness by comparison with the existing optimal results.

scholarly journals Genetic algorithm for the personnel assignment problem with multiple objectives

2007 ◽  
Vol 177 (3) ◽  
pp. 787-803 ◽  
Author(s):  
Ismail H. Toroslu ◽  
Yilmaz Arslanoglu
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Multiple Objectives
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