Optimization of nonlinear geological structure mapping using hybrid neuro-genetic techniques

A fairly reasonable result was obtained for nonlinear engineering problems using the optimization techniques such as neural network, genetic algorithms, and fuzzy logic independently in the past. Increasingly, hybrid techniques are being used to solve the nonlinear problems to obtain a better output...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical and computer modelling Vol. 54; no. 11; pp. 2913 - 2922
Main Authors: Ganesan, T., Vasant, P., Elamvazuthi, I.
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.12.2011
Elsevier
Subjects:
algorithms
Calculus of variations and optimal control
engineering
Engineering problems
Exact sciences and technology
fuzzy logic
General logic
Genetic programming
Geological structure mapping
geophysics
Hybrid optimization
hybrids
Logic and foundations
Mathematical analysis
Mathematical logic, foundations, set theory
Mathematics
Methods of scientific computing (including symbolic computation, algebraic computation)
neural networks
Neuro-genetic programming
Nonlinear
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in mathematical programming, optimization and calculus of variations
Numerical methods in optimization and calculus of variations
Sciences and techniques of general use
surveys
Geological structure mapping
Nonlinear
Neuro-genetic programming
Engineering problems
Hybrid optimization
Genetic programming
Optimization method
Nonlinear problems
Neural network
Fuzzy logic
Numerical analysis
Genetic algorithm
Applied mathematics
Function minimization
Variational calculus
Mathematical model
Comparative study
Computer aided analysis
Mathematical programming
ISSN:0895-7177, 1872-9479
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A fairly reasonable result was obtained for nonlinear engineering problems using the optimization techniques such as neural network, genetic algorithms, and fuzzy logic independently in the past. Increasingly, hybrid techniques are being used to solve the nonlinear problems to obtain a better output. This paper discusses the use of neuro-genetic hybrid technique to optimize the geological structure mapping which is known as seismic survey. It involves minimization of objective function subject to the requirement of geophysical and operational constraints. In this work, the optimization was initially performed using genetic programming, and followed by hybrid neuro-genetic programming approaches. Comparative studies and analysis were then carried out on the optimized results. The results indicate that the hybrid neuro-genetic hybrid technique produced better results compared to the stand-alone genetic programming method.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0895-7177
1872-9479
DOI:10.1016/j.mcm.2011.07.012