Numerical and Evolutionary Optimization 2020

• Autore: Quiroz Marcela
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 online resource (364 p.)
• Soggetto topico: Mathematics & science; Research & information: general
• Soggetto non controllato: adaptive algorithm; artificial intelligence; base excitation; chaotic perturbation; CMOS differential pair; CMOSA; CMOTA; cognitive tasks; computational fluid dynamics; constraint handling; continuation; Convolutional Neural Network; COVID-19; decision maker profile; decision making process; decision-making process; deep learning; density estimators; derivative-free optimization; differential evolution; drainage rehabilitation; energy central; ensemble method; evolutionary algorithms; finite volume method; fixed point arithmetic; forecasting; FP16; fully linear models; hybrid evolutionary approach; Hybrid Simulated Annealing; incorporation of preferences; JSSP; kriging method; linear programming; liquid storage tanks; LSTM; Metropolis; Monte Carlo analysis; multi-criteria classification; Multi-Gene Genetic Programming; multi-objective evolutionary optimization; multi-objective optimization; multi-objective portfolio optimization problem; multiobjective descent; multiobjective optimization; optimization; optimization framework; optimization using preferences; outranking relationships; overflooding; Pareto Tracer; pipe breaking; profile assessment; project portfolio selection problem; protein structure prediction; pseudo random number generator; PVT variations; radial basis functions; recommender system; region of interest approximation; robust optimization; ROOT; steady state algorithms; structural biology; surrogate modeling; Template-Based Modeling; trapezoidal fuzzy numbers; trust region methods; usability evaluation; VCO
• 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