Numerical and Evolutionary Optimization
Lara, Adriana
Numerical and Evolutionary Optimization - MDPI - Multidisciplinary Digital Publishing Institute 2019 - 1 electronic resource (230 p.)
Open Access
This book was established after the 6th 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.
Creative Commons
English
books978-3-03921-817-2 9783039218172 9783039218165
10.3390/books978-3-03921-817-2 doi
model predictive control bulbous bow improvement differential evolution algorithm evolutionary multi-objective optimization location routing problem flexible job shop scheduling problem basic differential evolution algorithm metric measure spaces NEAT genetic algorithm multiobjective optimization improved differential evolution algorithm performance indicator rubber averaged Hausdorff distance mixture experiments U-shaped assembly line balancing Genetic Programming Local Search driving events surrogate-based optimization single component constraints crop planning Pareto front numerical simulations shape morphing genetic programming economic crops local search and jump search model order reduction optimal solutions EvoSpace risky driving intelligent transportation systems optimal control IV-optimality criterion Bloat decision space diversity modify differential evolution algorithm power means driving scoring functions open-source framework evolutionary computation differential evolution algorithm vehicle routing problem multi-objective optimization
Numerical and Evolutionary Optimization - MDPI - Multidisciplinary Digital Publishing Institute 2019 - 1 electronic resource (230 p.)
Open Access
This book was established after the 6th 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.
Creative Commons
English
books978-3-03921-817-2 9783039218172 9783039218165
10.3390/books978-3-03921-817-2 doi
model predictive control bulbous bow improvement differential evolution algorithm evolutionary multi-objective optimization location routing problem flexible job shop scheduling problem basic differential evolution algorithm metric measure spaces NEAT genetic algorithm multiobjective optimization improved differential evolution algorithm performance indicator rubber averaged Hausdorff distance mixture experiments U-shaped assembly line balancing Genetic Programming Local Search driving events surrogate-based optimization single component constraints crop planning Pareto front numerical simulations shape morphing genetic programming economic crops local search and jump search model order reduction optimal solutions EvoSpace risky driving intelligent transportation systems optimal control IV-optimality criterion Bloat decision space diversity modify differential evolution algorithm power means driving scoring functions open-source framework evolutionary computation differential evolution algorithm vehicle routing problem multi-objective optimization