Elementary decision theory
Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Using expectation-maximization for reinforcement learning
Neural Computation
Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
Stochastic optimization and the simultaneous perturbation method
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Evolving neural networks through augmenting topologies
Evolutionary Computation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
An analysis of mutative σ-self-adaptation on linear fitness functions
Evolutionary Computation
Reinforcement learning by reward-weighted regression for operational space control
Proceedings of the 24th international conference on Machine learning
Efficient non-linear control through neuroevolution
ECML'06 Proceedings of the 17th European conference on Machine Learning
Anticipatory Behavior in Adaptive Learning Systems
A natural evolution strategy with asynchronous strategy updates
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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We present Fitness Expectation Maximization (FEM), a novel method for performing `black box' function optimization. FEM searches the fitness landscape of an objective function using an instantiation of the well-known Expectation Maximization algorithm, producing search points to match the sample distribution weighted according to higher expected fitness. FEM updates both candidate solution parameters and the search policy, which is represented as a multinormal distribution. Inheriting EM's stability and strong guarantees, the method is both elegant and competitive with some of the best heuristic search methods in the field, and performs well on a number of unimodal and multimodal benchmark tasks. To illustrate the potential practical applications of the approach, we also show experiments on finding the parameters for a controller of the challenging non-Markovian double pole balancing task.