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GECCO
2007
Springer
558views Optimization» more  GECCO 2007»
15 years 3 months ago
A chain-model genetic algorithm for Bayesian network structure learning
Bayesian Networks are today used in various fields and domains due to their inherent ability to deal with uncertainty. Learning Bayesian Networks, however is an NP-Hard task [7]....
Ratiba Kabli, Frank Herrmann, John McCall
GECCO
2007
Springer
181views Optimization» more  GECCO 2007»
15 years 1 months ago
Learning recursive programs with cooperative coevolution of genetic code mapping and genotype
The Probabilistic Adaptive Mapping Developmental Genetic Programming (PAM DGP) algorithm that cooperatively coevolves a population of adaptive mappings and associated genotypes is...
Garnett Carl Wilson, Malcolm I. Heywood
GECCO
2003
Springer
112views Optimization» more  GECCO 2003»
15 years 2 months ago
Multi-agent Learning of Heterogeneous Robots by Evolutionary Subsumption
Abstract. Many multi-robot systems are heterogeneous cooperative systems, systems consisting of different species of robots cooperating with each other to achieve a common goal. T...
Hongwei Liu, Hitoshi Iba
NIPS
2004
14 years 11 months ago
Log-concavity Results on Gaussian Process Methods for Supervised and Unsupervised Learning
Log-concavity is an important property in the context of optimization, Laplace approximation, and sampling; Bayesian methods based on Gaussian process priors have become quite pop...
Liam Paninski
CORR
2007
Springer
73views Education» more  CORR 2007»
14 years 9 months ago
Universal Reinforcement Learning
—We consider an agent interacting with an unmodeled environment. At each time, the agent makes an observation, takes an action, and incurs a cost. Its actions can influence futu...
Vivek F. Farias, Ciamac Cyrus Moallemi, Tsachy Wei...