A reduced model technique based on a reduced number of numerical simulations at a subset of operating conditions for a perfectly stirred reactor is developed in order to increase t...
Lionel Elliott, Derek B. Ingham, Adrian G. Kyne, N...
Genetic algorithms (GAs) are efficient non-gradient stochastic search methods. Parallel GAs are proposed to overcome the deficiencies of sequential GAs, such as low speed and aptn...
Baowen Xu, Yu Guan, Zhenqiang Chen, Karl R. P. H. ...
This paper presents results of the BBOB-2009 benchmarking of 31 search algorithms on 24 noiseless functions in a black-box optimization scenario in continuous domain. The runtime ...
Nikolaus Hansen, Anne Auger, Raymond Ros, Steffen ...
Heuristic search for the global minimum is studied. This paper is focused on the adaptation of control parameters in differential evolution (DE) and in controlled random search (...
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is usin...