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Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'06)
Timisoara, Romania
September 26-September 29
ISBN: 0-7695-2740-X
Ajith Abraham, Yonsei University, Korea
Evolutionary algorithms have become an important problem solving methodology among many researchers working in the area of computational intelligence. The population based collective learning process; self adaptation and robustness are some of the key features of evolutionary algorithm when compared to other global optimization techniques. Due to its simplicity, evolutionary algorithms have been widely accepted for solving several important practical applications in engineering, business, commerce etc. However, experimental evidence had indicated cases where evolutionary algorithms are inefficient at fine tuning solutions, but better at finding global basins of attraction.
Citation:
Ajith Abraham, "Tuning Evolutionary Algorithm Performance Using Nature Inspired Heuristics," synasc, pp.13, Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'06), 2006
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