Evolutionary Computing

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Author(s): Thomas E. Potok (Oak Ridge National Laboratory, USA), Xiaohui Cui (Oak Ridge National Laboratory, USA)and Yu Jiao (Oak Ridge National Laboratory, USA)
Copyright: 2009
Pages: 12
Source title: Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery
Source Author(s)/Editor(s): Hsiao-Fan Wang (National Tsing Hua University, ROC)
DOI: 10.4018/978-1-59904-982-3.ch008

Keywords: Data Mining and Databases / Information Science Reference / Knowledge Discovery / Library & Information Science

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Abstract

The rate at which information overwhelms humans is significantly more than the rate at which humans have learned to process, analyze, and leverage this information. To overcome this challenge, new methods of computing must be formulated, and scientist and engineers have looked to nature for inspiration in developing these new methods. Consequently, evolutionary computing has emerged as new paradigm for computing, and has rapidly demonstrated its ability to solve real-world problems where traditional techniques have failed. This field of work has now become quite broad and encompasses areas ranging from artificial life to neural networks. This chapter specifically focuses on two sub-areas of nature-inspired computing: Evolutionary Algorithms and Swarm Intelligence.

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