Numerical and Evolutionary Optimization 2020

doi:10.3390/books978-3-0365-1670-7

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Numerical and Evolutionary Optimization 2020

Edited by

August 2021

364 pages

ISBN978-3-0365-1669-1 (Hardback)
ISBN978-3-0365-1670-7 (PDF)

https://doi.org/10.3390/books978-3-0365-1670-7

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This book is a reprint of the Special Issue Numerical and Evolutionary Optimization 2020 that was published in

Computer Science & Mathematics

Summary

This book was established after the 8th 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

robust optimization; differential evolution; ROOT; optimization framework; drainage rehabilitation; overflooding; pipe breaking; VCO; differential evolution; CMOS differential pair; PVT variations; Monte Carlo analysis; multi-objective optimization; Pareto Tracer; continuation; constraint handling; surrogate modeling; multiobjective optimization; evolutionary algorithms; kriging method; ensemble method; adaptive algorithm; liquid storage tanks; base excitation; artificial intelligence; Multi-Gene Genetic Programming; computational fluid dynamics; finite volume method; JSSP; CMOSA; CMOTA; chaotic perturbation; differential evolution; fixed point arithmetic; FP16; pseudo random number generator; incorporation of preferences; multi-criteria classification; decision-making process; multi-objective evolutionary optimization; outranking relationships; decision maker profile; profile assessment; region of interest approximation; optimization using preferences; hybrid evolutionary approach; forecasting; Convolutional Neural Network; LSTM; COVID-19; deep learning; multiobjective optimization; trust region methods; multiobjective descent; derivative-free optimization; radial basis functions; fully linear models; decision making process; cognitive tasks; recommender system; project portfolio selection problem; usability evaluation; multi-objective optimization; multi-objective portfolio optimization problem; trapezoidal fuzzy numbers; density estimators; steady state algorithms; protein structure prediction; Hybrid Simulated Annealing; Template-Based Modeling; structural biology; Metropolis; optimization; linear programming; energy central