Searched for: subject%3A%22Multi%255C-objective%255C%252Boptimisation%22
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document
Bartlett, A.J. (author), Liem, C.C.S. (author), Panichella, A. (author)
Deep learning (DL) models are known to be highly accurate, yet vulnerable to adversarial examples. While earlier research focused on generating adversarial examples using whitebox strategies, later research focused on black-box strategies, as models often are not accessible to external attackers. Prior studies showed that black-box approaches...
conference paper 2023
document
Panichella, A. (author)
A key idea in many-objective optimization is to approximate the optimal Pareto front using a set of representative non-dominated solutions. The produced solution set should be close to the optimal front (convergence) and well-diversified (diversity). Recent studies have shown that measuring both convergence and diversity depends on the shape (or...
conference paper 2022
document
Janssen, P. (author)
Designers interested in applying evo-devo-design methods for performance based multi-objective design exploration have typically faced two main hurdles: itÂ’s too hard and too slow. An evo-devo-design method is proposed that effectively overcomes the hurdles of skill and speed by leveraging two key technologies: computational workflows and cloud...
conference paper 2013
document
Clarich, A. (author), Carlo, P. (author), Valentino, P. (author)
This presentation deals with industrial applications, in aeronautic and turbomachinery fields, of multi-objective and robust design optimisation, through the utilisation of the multi-objective optimisation and design environment modeFrontier. This code allows the easy process integration of any CAD/CAE commercial tool, and drives the designer in...
conference paper 2006
Searched for: subject%3A%22Multi%255C-objective%255C%252Boptimisation%22
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