Adaptive Differential Evolution : A Robust Approach to Multimodal Problem Optimization Hardback
Part of the Adaptation, Learning, and Optimization series
I ?rst met Jingqiao when he had just commenced his PhD research in evolutionary algorithms with Arthur Sanderson at Rensselaer.
Jingqiao's goals then were the investigation and development of a novel class of se- adaptivedi?erentialevolutionalgorithms,later calledJADE.
I had remarked to Jingqiao then that Arthur always appreciated strong theoretical foun- tions in his research, so Jingqiao's prior mathematically rigorous work in communications systems would be very useful experience.
Later in 2007, whenJingqiaohadcompletedmostofthetheoreticalandinitialexperimental work on JADE, I invited him to spend a year at GE Global Research where he applied his developments to several interesting and important real-world problems.
Most evolutionary algorithm conferences usually have their share of in- vative algorithm oriented papers which seek to best the state of the art - gorithms.
The best algorithms of a time-frame create a foundation for a new generationof innovativealgorithms, and so on, fostering a meta-evolutionary search for superior evolutionary algorithms. In the past two decades, during whichinterest andresearchin evolutionaryalgorithmshavegrownworldwide by leaps and bounds, engaging the curiosity of researchers and practitioners frommanydiversescienceandtechnologycommunities,developingstand-out algorithms is getting progressively harder.
- Format: Hardback
- Pages: 164 pages, 22 Tables, black and white; XIII, 164 p.
- Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
- Publication Date: 02/09/2009
- Category: Operational research
- ISBN: 9783642015267
- Paperback from £116.55
- PDF from £93.08