A Genetic Programming Approach to the Matrix Bandwidth-Minimization Problem

2010 ◽  
pp. 482-491
Author(s):  
Behrooz Koohestani ◽  
Riccardo Poli
Keyword(s):  
Genetic Programming ◽  
Minimization Problem ◽  
Programming Approach ◽  
The Matrix ◽  
Bandwidth Minimization ◽  
Matrix Bandwidth

scholarly journals GRASP and path relinking for the matrix bandwidth minimization

2004 ◽  
Vol 153 (1) ◽  
pp. 200-210 ◽  
Author(s):  
Estefanı́a Piñana ◽  
Isaac Plana ◽  
Vicente Campos ◽  
Rafael Martı́
Keyword(s):  
Path Relinking ◽  
The Matrix ◽  
Bandwidth Minimization ◽  
Matrix Bandwidth

scholarly journals A branch and bound algorithm for the matrix bandwidth minimization

2008 ◽  
Vol 186 (2) ◽  
pp. 513-528 ◽  
Author(s):  
Rafael Martí ◽  
Vicente Campos ◽  
Estefanía Piñana
Keyword(s):  
Branch And Bound ◽  
Branch And Bound Algorithm ◽  
The Matrix ◽  
Bandwidth Minimization ◽  
Matrix Bandwidth

A FAST ALGORITHM FOR BANDWIDTH MINIMIZATION

2007 ◽  
Vol 16 (03) ◽  
pp. 537-544 ◽  
Author(s):  
ANDREW LIM ◽  
BRIAN RODRIGUES ◽  
FEI XIAO
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Minimization Problem ◽  
Fast Algorithm ◽  
Hill Climbing ◽  
Solution Quality ◽  
Complete Problem ◽  
Np Complete ◽  
Bandwidth Minimization ◽  
Matrix Bandwidth

We propose a simple and direct node shifting method with hill climbing for the well-known matrix bandwidth minimization problem. Many heuristics have been developed for this NP-complete problem including the Cuthill-McKee (CM) and the Gibbs, Poole and Stockmeyer (GPS) algorithms. Recently, heuristics such as Simulated Annealing, Tabu Search and GRASP have been used, where Tabu Search and the GRASP with Path Relinking achieved significantly better solution quality than the CM and GPS algorithms. Experimentation shows that our method achieves the best solution quality when compared with these while being much faster than newly-developed algorithms.

scholarly journals Binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances

2016 ◽  
Vol 24 (1) ◽  
pp. 143-182 ◽  
Author(s):  
Harith Al-Sahaf ◽  
Mengjie Zhang ◽  
Mark Johnston
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Genetic Programming ◽  
Visual System ◽  
Image Classification ◽  
Human Visual System ◽  
Binary Classification ◽  
Programming Approach ◽  
Data Sets ◽  
New Class

In the computer vision and pattern recognition fields, image classification represents an important yet difficult task. It is a challenge to build effective computer models to replicate the remarkable ability of the human visual system, which relies on only one or a few instances to learn a completely new class or an object of a class. Recently we proposed two genetic programming (GP) methods, one-shot GP and compound-GP, that aim to evolve a program for the task of binary classification in images. The two methods are designed to use only one or a few instances per class to evolve the model. In this study, we investigate these two methods in terms of performance, robustness, and complexity of the evolved programs. We use ten data sets that vary in difficulty to evaluate these two methods. We also compare them with two other GP and six non-GP methods. The results show that one-shot GP and compound-GP outperform or achieve results comparable to competitor methods. Moreover, the features extracted by these two methods improve the performance of other classifiers with handcrafted features and those extracted by a recently developed GP-based method in most cases.

scholarly journals A NEW LINEAR GENETIC PROGRAMMING APPROACH BASED ON STRAIGHT LINE PROGRAMS: SOME THEORETICAL AND EXPERIMENTAL ASPECTS

2009 ◽  
Vol 18 (05) ◽  
pp. 757-781 ◽  
Author(s):  
CÉSAR L. ALONSO ◽  
JOSÉ LUIS MONTAÑA ◽  
JORGE PUENTE ◽  
CRUZ ENRIQUE BORGES
Keyword(s):  
Data Structure ◽  
Genetic Programming ◽  
Computer Programs ◽  
Symbolic Regression ◽  
Programming Approach ◽  
Linear Genetic Programming ◽  
Straight Line ◽  
Structured Representations ◽  
Regression Problems ◽  
Straight Line Programs

Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described, new recombination operators for GP related to slp's are introduced and a study of the Vapnik-Chervonenkis dimension of families of slp's is done. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representations.

Concise Equations for Rotor Dynamics Analysis

1995 ◽  
Author(s):  
W. J. Chen
Keyword(s):  
Equations Of Motion ◽  
Rotor Dynamics ◽  
Memory Storage ◽  
The Other ◽  
Dynamics Analysis ◽  
Real Domain ◽  
The Matrix ◽  
System Equations ◽  
Ordering Algorithms ◽  
Matrix Bandwidth

Abstract Concise equations for rotor dynamics analysis are presented. Two coordinate ordering methods are introduced in the element equations of motion. One is in the real domain and the other is in the complex domain. The two proposed ordering algorithms lead to more compact element matrices. A station numbering technique is also proposed for the system equations during the assembly process. This numbering technique can minimize the matrix bandwidth, the memory storage and can increase the computational efficiency.

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