In many practical engineering design and other scientific optimization problems, the
objective function is not given in closed form in terms of the design variables. Given the …

Surrogate-assisted evolutionary optimization has proved to be effective in reducing
optimization time, as surrogates, or meta-models can approximate expensive fitness …

A finite element (FE)–guided mathematical surrogate modeling methodology is presented
for evaluating relative injury trends across varied vehicular impact conditions. The …

Various neural-network based surrogate model construction methods are disclosed herein,
along with various applications of such models. Designed for use when only a sparse …

In this paper, an evolutionary algorithm with spatially distributed surrogates (EASDS) for
multiobjective optimization is presented. The algorithm performs actual analysis for the initial …

Government agencies, globally, often strive to minimize the risk of human death and serious
injury on road transport systems. Multi-national projects like Vision Zero have been …

Multivariate adaptive regression spline (MARS) is a statistical modeling method used to
represent a complex system. More recently, a version of MARS was modified to be …

Optimizing expensive black-box systems with limited data is an extremely challenging
problem. As a resolution, we present a new surrogate optimization approach by addressing …

This paper introduces an evolutionary algorithm with Multiple Adaptive Spatially Distributed
Surrogates (MASDS) for multi-objective optimisation. The core optimisation algorithm is a …

This paper proposes a procedure to improve the design of an automobile crashworthiness
using the deceleration pulse in a simplified form as a design variable. A complete vehicle in …