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(JW's Restaurant)
Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of
Evolutionary Algorithms
Overview: A
black-box optimization problem may be defined by the set of all potential
solutions and a procedure for evaluating competing solutions. The
separation of problem specifics from an optimizer enables a
straightforward formulation of many different real-world problems within
this framework. That is why the design of robust and scalable black-box
optimization techniques has become one of the most important challenges in
computational optimization. This talk focuses on an advanced black-box
optimization algorithm called the hierarchical Bayesian optimization
algorithm (hBOA), which derives inspiration from evolutionary computation
on one hand and machine learning on the other. The talk motivates the
algorithm, describes its basic procedure, and presents interesting results
of its application to challenging real-world problems. The talk also lists
important directions for future research in this area.
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