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GECCO
2011
Springer
276views Optimization» more  GECCO 2011»
14 years 3 months ago
Evolution of reward functions for reinforcement learning
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
Scott Niekum, Lee Spector, Andrew G. Barto
AAAI
2011
13 years 12 months ago
Mean Field Inference in Dependency Networks: An Empirical Study
Dependency networks are a compelling alternative to Bayesian networks for learning joint probability distributions from data and using them to compute probabilities. A dependency ...
Daniel Lowd, Arash Shamaei
AAAI
2011
13 years 12 months ago
Optimal Rewards versus Leaf-Evaluation Heuristics in Planning Agents
Planning agents often lack the computational resources needed to build full planning trees for their environments. Agent designers commonly overcome this finite-horizon approxima...
Jonathan Sorg, Satinder P. Singh, Richard L. Lewis
CEC
2011
IEEE
13 years 11 months ago
Effective ranking + speciation = Many-objective optimization
—Multiobjective optimization problems have been widely addressed using evolutionary computation techniques. However, when dealing with more than three conflicting objectives (th...
Mario Garza-Fabre, Gregorio Toscano Pulido, Carlos...
CEC
2011
IEEE
13 years 11 months ago
Accelerating convergence towards the optimal pareto front
—Evolutionary algorithms have been very popular optimization methods for a wide variety of applications. However, in spite of their advantages, their computational cost is still ...
Mohsen Davarynejad, Jafar Rezaei, Jos L. M. Vranck...