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Numerical and Evolutionary Optimization 2018
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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.
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Keywords
- averaged Hausdorff distance
- basic differential evolution algorithm
- Bloat
- bulbous bow
- crop planning
- decision space diversity
- differential evolution algorithm
- driving events
- driving scoring functions
- economic crops
- Evolutionary computation
- evolutionary multi-objective optimization
- EvoSpace
- flexible job shop scheduling problem
- genetic algorithm
- genetic programming
- improved differential evolution algorithm
- improvement differential evolution algorithm
- intelligent transportation systems
- IV-optimality criterion
- Local Search
- local search and jump search
- location routing problem
- metric measure spaces
- mixture experiments
- model order reduction
- Model Predictive Control
- modify differential evolution algorithm
- multi-objective optimization
- multiobjective optimization
- NEAT
- Numerical simulations
- open-source framework
- Optimal Control
- optimal solutions
- Pareto front
- performance indicator
- power means
- risky driving
- Rubber
- shape morphing
- single component constraints
- surrogate-based optimization
- U-shaped assembly line balancing
- vehicle routing problem
Links
DOI: 10.3390/books978-3-03921-817-2Editions
