A large collection of
global optimization problems with descriptions, code, and examples.
Global Minimum is described in J. Mockus, Bayesian Approach to Global Optimization.Kluwer Academic Publishers , Dordrecht, 1989. You can also get the software at:
How to solve, simplify, or focus the problems involving control and optimization of complex dynamic systems via use of domain specific visualizations.
J. Mockus, W.F. Eddy, A. Mockus, L. Mockus, and G. Reklaitis. Bayesian Heuristic Approach to Discrete and Global Optimization. Kluwer Academic Publishers, Dordrecht, 1997.
W. F. Eddy and A. Mockus. Dynamic Visualization in Modelling and Optimization of Ill Defined Problems. In C.A. Floudas and P.M. Pardalos, editor, State of the Art in Global Optimization: computational methods and applications, 499-520. Kluwer Academic Publishers, Dordrecht, 1996.
A. Mockus, J. Mockus, and L. Mockus. Discrete Optimization,Information Based Complexity, and Bayesian Heuristics Approach. In Proceedings of International Symposium on Operations Research with Applications in Engineering Technology and Management (ISORA'95), August 19-22 , 1995. Beijing, China.
A. Mockus, J. Mockus, and L. Mockus. Adapting Stochastic and Heuristic Methods for Discrete Optimization Problems. Informatica, 5(1):123-166, 1994.
A. Mockus, J. Mockus, and L. Mockus. Bayesian Approach Adapting Stochastic and Heuristic Methods of Global and Discrete Optimization. In Abstracts, Second World Meeting, International Society for Bayesian Analysis, pages 10-11, 1994. Alicante, Spain.
A. Mockus and L. Mockus. Designing Software for Global Optimization. Informatica, 1(1):71-88, 1990.