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Adaptive Differential Evolution : A Robust Approach to Multimodal Problem Optimization, Paperback / softback Book

Adaptive Differential Evolution : A Robust Approach to Multimodal Problem Optimization Paperback / softback

Part of the Adaptation, Learning, and Optimization series

Paperback / softback

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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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