DocumentCode :
2497333
Title :
Opposition based computing — A survey
Author :
Al-Qunaieer, Fares S. ; Tizhoosh, Hamid R. ; Rahnamayan, Shahryar
Author_Institution :
Syst. Design Eng., Univ. of Waterloo, Waterloo, ON, Canada
fYear :
2010
fDate :
18-23 July 2010
Firstpage :
1
Lastpage :
7
Abstract :
In algorithms design, one of the important aspects is to consider efficiency. Many algorithm design paradigms are existed and used in order to enhance algorithms´ efficiency. Opposition-based Learning (OBL) paradigm was recently introduced as a new way of thinking during the design of algorithms. The concepts of opposition have already been used and applied in several applications. These applications are from different fields, such as optimization algorithms, learning algorithms and fuzzy logic. The reported results confirm that OBL paradigm was promising to accelerate or to enhance accuracy of soft computing algorithms. In this paper, a survey of existing applications of opposition-based computing is presented.
Keywords :
fuzzy logic; learning (artificial intelligence); optimisation; algorithm design; fuzzy logic; learning algorithms; opposition based computing; opposition-based learning paradigm; optimization algorithms; soft computing algorithms; Accuracy; Algorithm design and analysis; Artificial neural networks; Benchmark testing; Convergence; Learning; Optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2010 International Joint Conference on
Conference_Location :
Barcelona
ISSN :
1098-7576
Print_ISBN :
978-1-4244-6916-1
Type :
conf
DOI :
10.1109/IJCNN.2010.5596906
Filename :
5596906
Link To Document :
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