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

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@Article{Ganesan20112913,

• author = "T. Ganesan and P. Vasant and I. Elamvazuthi",
• title = "Optimization of nonlinear geological structure mapping using hybrid neuro-genetic techniques",
• journal = "Mathematical and Computer Modelling",
• volume = "54",
• number = "11-12",
• pages = "2913--2922",
• year = "2011",
• ISSN = "0895-7177",
• DOI = "doi:10.1016/j.mcm.2011.07.012",
• URL = "http://www.sciencedirect.com/science/article/pii/S0895717711004225",
• keywords = "genetic algorithms, genetic programming, Nonlinear, Engineering problems, Geological structure mapping, Hybrid optimisation",
• abstract = "A fairly reasonable result was obtained for nonlinear engineering problems using the optimisation 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 optimise the geological structure mapping which is known as seismic survey. It involves minimisation 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 optimised results. The results indicate that the hybrid neuro-genetic hybrid technique produced better results compared to the stand-alone genetic programming method.",

}

Genetic Programming entries for Timothy Ganesan Pandian Vasant I Elamvazuthi

Citations