• Jingqiao Zhang
  • Arthur C. Sanderson
Part of the Adaptation Learning and Optimization book series (ALO, volume 1)


Optimization problems are ubiquitous in academic research and real-world applications such as in engineering, finance, and scientific areas. What coefficients of a neural network minimize classification errors? What combination of bids maximizes the outcome in an auction? What variable- and check-node distributions optimize a low-density parity-check code design? In general, optimization problems arise wherever such resources as space, time and cost are limited. With no doubt, researchers and practitioners need an efficient and robust optimization approach to solve problems of different characteristics that are fundamental to their daily work.


Differential Evolution Spherical Function Combinatorial Auction Convergence Performance Evolutionary Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jingqiao Zhang
    • Arthur C. Sanderson

      There are no affiliations available

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