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ECAI
2010
Springer
15 years 5 months ago
Bayesian Monte Carlo for the Global Optimization of Expensive Functions
In the last decades enormous advances have been made possible for modelling complex (physical) systems by mathematical equations and computer algorithms. To deal with very long run...
Perry Groot, Adriana Birlutiu, Tom Heskes
GECCO
2008
Springer
139views Optimization» more  GECCO 2008»
15 years 5 months ago
Voronoi-initializated island models for solving real-coded deceptive problems
Deceptive problems have always been considered difficult for Genetic Algorithms. To cope with this characteristic, the literature has proposed the use of Parallel Genetic Algorith...
Santiago Muelas, José Manuel Peña, V...
GECCO
2008
Springer
117views Optimization» more  GECCO 2008»
15 years 5 months ago
Is "best-so-far" a good algorithmic performance metric?
In evolutionary computation, experimental results are commonly analyzed using an algorithmic performance metric called best-so-far. While best-so-far can be a useful metric, its u...
Nathaniel P. Troutman, Brent E. Eskridge, Dean F. ...
GECCO
2008
Springer
274views Optimization» more  GECCO 2008»
15 years 5 months ago
Bacterial foraging oriented by particle swarm optimization strategy for PID tuning
Proportional integral derivative (PID) controller tuning is an area of interest for researchers in many disciplines of science and engineering. This paper presents a new algorithm...
Wael Mansour Korani
ICML
2010
IEEE
15 years 5 months ago
Learning Markov Logic Networks Using Structural Motifs
Markov logic networks (MLNs) use firstorder formulas to define features of Markov networks. Current MLN structure learners can only learn short clauses (4-5 literals) due to extre...
Stanley Kok, Pedro Domingos
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