Abstract. This paper presents a new approach to optimize gait parameter sets using evolutionary algorithms. It separates the crossover-step of the evolutionary algorithm into an in...
We develop a methodology for evaluating a decision strategy generated by a stochastic optimization model. The methodology is based on a pilot study in which we estimate the distri...
Robert Rush, John M. Mulvey, John E. Mitchell, Tho...
We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares stati...
A new model for evolving the structure of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. The model is a hybrid technique that combines a Genetic Algorithm...
Relying on optimally distinguishable distributions (ODD), it was defined very recently a new framework for the composite hypothesis testing. We resort to the linear model to inve...