Ricerc@Sapienza

The paper presents an overview of global issues in optimizationmethods for training feedforward
neural networks (FNN) in a regression setting.We first recall the learning optimization
paradigm for FNN and we briefly discuss global scheme for the joint choice of the network
topologies and of the network parameters. The main part of the paper focuses on the
core subproblem which is the continuous unconstrained (regularized) weights optimization
problem with the aim of reviewing global methods specifically arising both in multi layer

The paper presents a multi-objective derivative-free and deterministic global/local hybrid algorithm for the efficient and effective solution of simulation-based design optimization (SBDO) problems. The objective is to show how the hybridization of two multi-objective derivative-free global and local algorithms achieves better performance than the separate use of the two algorithms in solving specific SBDO problems for hull-form design.