Numerical and Evolutionary Optimization 2020

• Autore: Quiroz Marcela
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 electronic resource (364 p.)
• Soggetto topico: Research & information: general; Mathematics & science
• Soggetto non controllato: robust optimization; differential evolution; ROOT; optimization framework; drainage rehabilitation; overflooding; pipe breaking; VCO; 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; 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; 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 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
• Persona (resp. second.): SchützeOliver; RuizJuan Gabriel; de la FragaLuis Gerardo; QuirozMarcela
• Sommario/riassunto: 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.
• Titolo autorizzato: Numerical and Evolutionary Optimization 2020
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557631003321
• Lo trovi qui: Univ. Federico II