We study the benefits of Genetic Algorithms, in particular the crossover operator, in constructing experimental designs that are D-optimal. To this purpose, we use standard Monte Carlo algorithms such as DETMAX and k-exchange as the mutation operator in a Genetic Algorithm. Compared to the heuristics, our algorithms are slower but yield better results. Key-Words: Genetic Algorithm, Memetic Algo...

Designing optimal experiments minimizes the uncertainty of results and maximizes efficient use resources. Herein, machine learning surrogate models approximate coordinate exchange (ACE) algorithm are used to determine experimental designs (OEDs) over large or arbitrarily restrictive design spaces. OED is particularly salient in materials science, where expensive material properties must often b...

Abstract Structural reliability analysis is concerned with estimation of the probability a critical event taking place, described by $$P(g(\mathbf{X} ) \le 0)$$ P ( g X ) ≤ 0 for some n...