Evolutionary computing

Robert M. Patton; Xiaohui Cui; Yu Jiao; Thomas E. Potok

doi:10.4018/978-1-59904-982-3.ch008

Evolutionary computing

Robert M. Patton, Xiaohui Cui, Yu Jiao, Thomas E. Potok

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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.

Original language	English
Title of host publication	Intelligent Data Analysis
Subtitle of host publication	Developing New Methodologies Through Pattern Discovery and Recovery
Publisher	IGI Global
Pages	131-142
Number of pages	12
ISBN (Print)	9781599049823
DOIs	https://doi.org/10.4018/978-1-59904-982-3.ch008
State	Published - 2008

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ER -

Evolutionary computing

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