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Numerical and Evolutionary Optimization 2024
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This Special Issue was inspired by the 11th International Workshop on Numerical and Evolutionary Optimization (NEO 2024), held from 3 to 6 September 2024 in Mexico City, Mexico, and hosted by Cinvestav. Solving real-world scientific and engineering problems has always been a challenge, and the complexity of these tasks has increased in recent years as more sources of data and information have been continuously developed. Thus, the design and analysis of powerful search and optimization techniques is of great importance. Two well-established fields that focus on this task are (i) traditional numerical optimization techniques and (ii) bio-inspired metaheuristic methods. Both of these general approaches have unique strengths and weaknesses, allowing researchers to solve certain challenging problems while failing to solve others. The goal of the NEO workshop series is to gather experts from both fields to discuss, compare, and merge these complementary perspectives. Collaborative work allows researchers to maximize the strengths and minimize the weaknesses of both paradigms. NEO also intends to help researchers in these fields to understand and tackle real-world problems like pattern recognition, routing, energy, lines of production, prediction, and modeling, among others.
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Keywords
- A
- A posterior preference articulation
- accelerometry
- adaptive adjustment
- adaptive control
- Adaptive observer
- Air quality
- artificial neural network
- Artificial Neural Networks
- Automated welding inspection
- Bin packing problem
- branch and bound
- Cardiovascular system
- classification models
- Climate Change
- Cloud computing
- CO2
- Compact invariant sets
- Computer vision in manufacturing
- Content distribution networks
- Contrast and detail
- controller design
- cost reduction
- cyber–physical system
- deep learning
- denial-of-service attack (DoS)
- Diabetes mellitus model
- differential evolution
- ensemble methods
- estimation of distribution algorithm
- evolutionary algorithms
- evolutionary strategy
- False data injection attack (FDI)
- fault detection and isolation
- feature selection
- forecast
- Forecasting
- Generalized dynamic observer
- Generalized Mallows model
- grouping genetic algorithm
- Heart Diseases
- heuristics
- human activity recognition
- image enhancement
- Improved local search
- Insulin observation
- Internet shopping problem
- Investment portfolios
- leak detection
- Lipschitz nonlinearities
- Markovian logic
- memetic algorithm
- Mixed no-idle permutation flow shop scheduling
- multi-objective optimization
- Multiclass classification
- multivariate time series
- Mutation operator
- N
- Neuro-fuzzy system
- nonlinear analysis
- nonlinear system
- normal form
- NSGA-II
- optimization
- particle swarm
- particle swarm optimization
- physical activity
- Pipeline diagnosis
- Pressure–volume loops
- principal component analysis
- Radius of curvature
- Random data injection attack (RDI)
- remote monitoring
- Self-balancing inverted pendulum
- sensor faults
- Sigmoid transformation
- sliding mode observer
- Takagi–Sugeno system (T-S)
- Technique diversification
- Thau observer
- Trajectory Tracking
- TS system
- Unobservable states
- vision transformer (ViT)
- water monitoring
- Weld defect detection
- Zonotopic Kalman filter
Links
DOI: 10.3390/books978-3-7258-5576-6Editions