Numerical and Evolutionary Optimization

Edited by
November 2019
230 pages
  • ISBN978-3-03921-816-5 (Paperback)
  • ISBN978-3-03921-817-2 (PDF)

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

Computer Science & Mathematics

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.

  • Paperback
© 2020 by the authors; CC BY license
genetic programming; driving scoring functions; driving events; risky driving; intelligent transportation systems; mixture experiments; single component constraints; genetic algorithm; IV-optimality criterion; multiobjective optimization; optimal control; model order reduction; model predictive control; location routing problem; rubber; modify differential evolution algorithm; vehicle routing problem; differential evolution algorithm; crop planning; economic crops; improvement differential evolution algorithm; averaged Hausdorff distance; evolutionary multi-objective optimization; power means; metric measure spaces; performance indicator; Pareto front; surrogate-based optimization; numerical simulations; shape morphing; bulbous bow; open-source framework; U-shaped assembly line balancing; basic differential evolution algorithm; improved differential evolution algorithm; optimal solutions; Genetic Programming; Bloat; NEAT; Local Search; EvoSpace; improved differential evolution algorithm; flexible job shop scheduling problem; local search and jump search; evolutionary computation; multi-objective optimization; decision space diversity