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

• 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
• DOI: 10.1016/j.mcm.2011.07.012

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.