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IJSTR >> Volume 5 - Issue 7, July 2016 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Evolutionary Algorithms For Neural Networks Binary And Real Data Classification

[Full Text]

 

AUTHOR(S)

Dr. Hanan A.R. Akkar, Firas R. Mahdi

 

KEYWORDS

Artificial neural networks, Classifications, Evolutionary algorithms, Population-based algorithms, Meta-heuristics techniques, and Optimization.

 

ABSTRACT

Artificial neural networks are complex networks emulating the way human rational neurons process data. They have been widely used generally in: prediction, clustering, classification, and association. The training algorithms that used to determine the network weights are almost the most important factor that influence the neural networks performance. Recently many meta-heuristic and Evolutionary algorithms are employed to optimize neural networks weights to achieve better neural performance. This paper aims to use recently proposed algorithms for optimizing neural networks weights comparing these algorithms performance with other classical meta-heuristic algorithms used for the same purpose. However, to evaluate the performance of such algorithms for training neural networks we examine such algorithms to classify four opposite binary XOR clusters and classification of continuous real data sets such as: Iris and Ecoli.

 

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