A Supervised Learning Approach Involving Active Subspaces for an Efficient Genetic Algorithm in High-Dimensional Optimization Problems

DOI10.1137/20M1345219MaRDI QIDQ5005000

Gianluigi Rozza, Nicola Demo, Marco Tezzele

Publication date: 4 August 2021

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2006.07282

zbMATH Keywords

genetic algorithmactive subspaceshigh-dimensional optimization

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Numerical optimization and variational techniques (65K10) Sensitivity, stability, parametric optimization (90C31)

Related Items (3)

Kernel‐based active subspaces with application to computational fluid dynamics parametric problems using the discontinuous Galerkin method ⋮ Multi‐fidelity data fusion through parameter space reduction with applications to automotive engineering ⋮ Global optimization using random embeddings

Uses Software

• MVF

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• A literature survey of benchmark functions for global optimisation problems
• Sequential design of computer experiments for the estimation of a probability of failure
• A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems
• The parallel genetic algorithm as function optimizer
• An algorithm for finding the Chebyshev center of a convex polyhedron
• Reduced order modeling for nonlinear structural analysis using Gaussian process regression
• Random gradient-free minimization of convex functions
• Experimental testing of advanced scatter search designs for global optimization of multimodal functions
• Bayesian Optimization in a Billion Dimensions via Random Embeddings
• A New Genetic Algorithm for the Quadratic Assignment Problem
• Active Subspaces
• Radial Basis Functions
• 1 Basic ideas and tools for projection-based model reduction of parametric partial differential equations
• Active Subspace of Neural Networks: Structural Analysis and Universal Attacks
• Gradient-Based Dimension Reduction of Multivariate Vector-Valued Functions
• Hit-and-Run Algorithms for Generating Multivariate Distributions
• Hit-and-Run from a Corner
• Genetic Algorithms and the Optimal Allocation of Trials
• A continuous genetic algorithm designed for the global optimization of multimodal functions

This page was built for publication: A Supervised Learning Approach Involving Active Subspaces for an Efficient Genetic Algorithm in High-Dimensional Optimization Problems