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Adaptive Differential Evolution - A Robust Approach to Multimodal Problem Optimization (Hardcover, 2009 ed.)
Loot Price: R2,886
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Adaptive Differential Evolution - A Robust Approach to Multimodal Problem Optimization (Hardcover, 2009 ed.)
Series: Adaptation, Learning, and Optimization, 1
Expected to ship within 10 - 15 working days
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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.
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