This paper explores an approach to global, stochastic, simulation optimization which combines stochastic approximation (SA) with simulated annealing (SAN). SA directs a search of ...
Abstract. Biological systems involving genetic reactions are large discrete event systems, and often contain certain species that occur in small quantities, and others that occur i...
We propose a general-purpose stochastic optimization algorithm, the so-called annealing stochastic approximation Monte Carlo (ASAMC) algorithm, for neural network training. ASAMC c...
Abstract. In this paper we propose an optimal anytime version of constrained simulated annealing (CSA) for solving constrained nonlinear programming problems (NLPs). One of the goa...
This paper presents a hierarchical approach on the simulation of large-scale discrete event systems used recently by Kiran Consulting Group (KCG) to model shipyard operations. Bec...