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Reactive Sample Size for Heuristic Search in Simulation-based Optimization
This paper presents a novel reactive sample size algorithm based on parametric tests and indifference-zone selection, which can be used for improving the efficiency and robustness of heuristic optimization methods.
A Telescopic Binary Learning Machine for Training Neural Networks
This paper proposes a new algorithm based on multi-scale stochastic local search with binary representation for training neural networks.
Learning Modulo Theories for preference elicitation in hybrid domains
CLEO iteratively alternates a preference elicitation step, where pairs of candidate solutions are selected based on the current utility model, and a refinement step where the utility is refined by incorporating the feedback received.
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