Numerical and Evolutionary Optimization 2021

doi:10.3390/books978-3-0365-7905-4

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Numerical and Evolutionary Optimization 2021

Edited by

June 2023

214 pages

ISBN978-3-0365-7904-7 (Hardback)
ISBN978-3-0365-7905-4 (PDF)

https://doi.org/10.3390/books978-3-0365-7905-4

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This book is a reprint of the Special Issue Numerical and Evolutionary Optimization 2021 that was published in

Computer Science & Mathematics

Summary

This reprint was established after the 9th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications.

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Hardback

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Keywords

distributor’s pallet loading problem; heuristics; bin packing; real-life instances; emergency medical services; emergency medicine; decision-support system; pre-hospital emergency care; ambulance response time; machine learning; geo-indistinguishability; differential privacy; privacy-preserving machine learning; input perturbation; estimation of distribution algorithm; Mallows model; moth-flame algorithm; job shop scheduling problem; quay crane scheduling problem; first-passage time; Markov chain; queueing theory; simulation; OR in health services; KPI; wind energy; wind turbine blades; erosion; modal analysis; aerodynamic analysis; AutoML; AutoML; feature selection; fault severity assessment; gearboxes; XGBoost classifiers; autism; attention; ASD; learning activities; EEG; BCI; features; artificial intelligence; machine learning; Grouping Genetic Algorithm; variable decomposition; Large-Scale Constrained Optimization; DVT; early diagnosis; artificial intelligence; machine-learning; smart system; embedded system; edge computing; edge device; OpenFOAM; CFD; ANFIS; ANFIS (GA); ANFIS (PSO); ANFIS (FFA); nonlinear programming; largest small polygons (LSP); {LSP(n)} model-class; optimal area sequence {A(n)}; revised LSP model; mathematica model development environment; IPOPT solver engine; numerical optimization results and regression model for estimating {A(n)}; n/a