Empirical studies on parallel network construction of Bayesian optimization algorithms

This paper discusses a parallel optimization algorithm based on evolutionary algorithms with probabilistic model-building in order to design a robust search algorithm that can be applicable to a wide-spectrum of application problems effectively and reliably. Probabilistic model building genetic algorithm, which is also called estimation of distribution algorithm, is a promising approach in evolutionary computation and its parallelization has been investigated. We propose an improvement of parallel network construction in distributed Bayesian optimization algorithms which estimate distribution of promising solutions as Bayesian networks. Through numerical experiments on an actual parallel architecture, we show the effectiveness of our approach compared to the conventional parallelization. Also we perform experiments on a real-world application problem: protein structure predictions.