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EMO
2006
Springer
182views Optimization» more  EMO 2006»
15 years 7 months ago
Multi-objective Pole Placement with Evolutionary Algorithms
Multi-Objective Evolutionary Algorithms (MOEA) have been succesfully applied to solve control problems. However, many improvements are still to be accomplished. In this paper a new...
Gustavo Sánchez, Minaya Villasana, Miguel S...
UAI
2000
15 years 5 months ago
The Complexity of Decentralized Control of Markov Decision Processes
We consider decentralized control of Markov decision processes and give complexity bounds on the worst-case running time for algorithms that find optimal solutions. Generalization...
Daniel S. Bernstein, Shlomo Zilberstein, Neil Imme...
AUSAI
2008
Springer
15 years 6 months ago
Using Gaussian Processes to Optimize Expensive Functions
The task of finding the optimum of some function f(x) is commonly accomplished by generating and testing sample solutions iteratively, choosing each new sample x heuristically on t...
Marcus R. Frean, Phillip Boyle
CEC
2010
IEEE
15 years 5 months ago
Elitist Artificial Bee Colony for constrained real-parameter optimization
Abstract-- A novel algorithm to solve constrained realparameter optimization problems, based on the Artificial Bee Colony algorithm is introduced in this paper. The operators used ...
Efrén Mezura-Montes, Ramiro Ernesto Velez-K...
AAAI
1994
15 years 5 months ago
Acting Optimally in Partially Observable Stochastic Domains
In this paper, we describe the partially observable Markov decision process pomdp approach to nding optimal or near-optimal control strategies for partially observable stochastic ...
Anthony R. Cassandra, Leslie Pack Kaelbling, Micha...