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» Solving Hierarchical Optimization Problems Using MOEAs
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CEC
2008
IEEE
15 years 10 months ago
Improved Particle Swarm Optimization with low-discrepancy sequences
— Quasirandom or low discrepancy sequences, such as the Van der Corput, Sobol, Faure, Halton (named after their inventors) etc. are less random than a pseudorandom number sequenc...
Millie Pant, Radha Thangaraj, Crina Grosan, Ajith ...
126
Voted
SC
1995
ACM
15 years 7 months ago
Distributing a Chemical Process Optimization Application Over a Gigabit Network
We evaluate the impact of a gigabit network on the implementation of a distributed chemical process optimization application. The optimization problem is formulated as a stochasti...
Robert L. Clay, Peter Steenkiste
160
Voted
ATAL
2011
Springer
14 years 3 months ago
Quality-bounded solutions for finite Bayesian Stackelberg games: scaling up
The fastest known algorithm for solving General Bayesian Stackelberg games with a finite set of follower (adversary) types have seen direct practical use at the LAX airport for o...
Manish Jain, Christopher Kiekintveld, Milind Tambe
132
Voted
ICML
2009
IEEE
16 years 4 months ago
Bandit-based optimization on graphs with application to library performance tuning
The problem of choosing fast implementations for a class of recursive algorithms such as the fast Fourier transforms can be formulated as an optimization problem over the language...
Arpad Rimmel, Frédéric de Mesmay, Ma...
155
Voted
CPAIOR
2008
Springer
15 years 5 months ago
Amsaa: A Multistep Anticipatory Algorithm for Online Stochastic Combinatorial Optimization
The one-step anticipatory algorithm (1s-AA) is an online algorithm making decisions under uncertainty by ignoring future non-anticipativity constraints. It makes near-optimal decis...
Luc Mercier, Pascal Van Hentenryck