• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.279-288
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• 1999

In many cases where the conventional binomial distribution fails to apply to real world data, it is mainly due to the lack of independence among Bernoulli trials. Several authors have proposed models that are useful when independence assumption is not satisfied. In this paper, one proposed model is adapted, and estimators of dependence related parameter that is crucial in defining that model are considered. Simulation is performed to compare two estimators(method of moment estimator and maximum likelihood estimator) of dependence related parameter, and conclusions are made.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.4
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• pp.412-424
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• 2022

Since traditional derivative-based optimization for noisy simulation shows bad performance, evolutionary algorithms are considered as substitutes. Especially in case when outputs are binary, more simulation trials are needed to get near-optimal solution since the outputs are discrete and have high and heterogeneous variance. In this paper, we propose a genetic algorithm called SARAGA which adopts dynamic resampling and fitness approximation using surrogate. SARAGA reduces unnecessary numbers of expensive simulations to estimate success probabilities estimated from binary simulation outputs. SARAGA allocates number of samples to each solution dynamically and sometimes approximates the fitness without additional expensive experiments. Experimental results show that this novel approach is effective and proper hyper parameter choice of surrogate and resampling can improve the performance of algorithm.