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Efficiency Analysis of Genetic Algorithm and Genetic Programming in Data Mining and Image Processing

Efficiency Analysis of Genetic Algorithm and Genetic Programming in Data Mining and Image Processing

Ayan Chatterjee, Mahendra Rong
Copyright: © 2017 |Pages: 27
ISBN13: 9781522524403|ISBN10: 1522524401|EISBN13: 9781522524410
DOI: 10.4018/978-1-5225-2440-3.ch016
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MLA

Chatterjee, Ayan, and Mahendra Rong. "Efficiency Analysis of Genetic Algorithm and Genetic Programming in Data Mining and Image Processing." Handbook of Research on Manufacturing Process Modeling and Optimization Strategies, edited by Raja Das and Mohan Pradhan, IGI Global, 2017, pp. 334-360. https://doi.org/10.4018/978-1-5225-2440-3.ch016

APA

Chatterjee, A. & Rong, M. (2017). Efficiency Analysis of Genetic Algorithm and Genetic Programming in Data Mining and Image Processing. In R. Das & M. Pradhan (Eds.), Handbook of Research on Manufacturing Process Modeling and Optimization Strategies (pp. 334-360). IGI Global. https://doi.org/10.4018/978-1-5225-2440-3.ch016

Chicago

Chatterjee, Ayan, and Mahendra Rong. "Efficiency Analysis of Genetic Algorithm and Genetic Programming in Data Mining and Image Processing." In Handbook of Research on Manufacturing Process Modeling and Optimization Strategies, edited by Raja Das and Mohan Pradhan, 334-360. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2440-3.ch016

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Abstract

Today, in the age of artificial intelligence and machine learning, Data mining and Image processing are two important platforms. GA and GP are value based and program based randomized searching tools respectively and these two are very much useful in the fields' data mining and image processing for handling different issues. In this chapter, a review is made on ability of GA and GP in some applications of these two fields. Here, the selected subfields of data mining are market analysis, fraud detection, risk management, sports analysis, protein interaction, classification of data, drug discovery and feature construction. The similar in image processing are enhancement and segmentation of images, face recognition, photo mosaic generation, data embedding, image pattern classification, object detection and Graphics Processor Unit (GPU) development. The efficiencies of GA and GP in these particular applications are analyzed with corresponding parameters, comparing with other non-GA and non-GP approaches of the corresponding subfields.

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