Bioinspired Optimization Methods and Their Applications
Springer International Publishing (Verlag)
978-3-031-21093-8 (ISBN)
The 19 full papers presented in this book were carefully reviewed and selected from 23 submissions.
The papers in this BIOMA proceedings specialized in bioinspired algorithms as a means for solving the optimization problems and came in two categories: theoretical studies and methodology advancements on the one hand, and algorithm adjustments and their applications on the other.
An agent-based model to investigate different behaviours in a crowd simulation.- Accelerating Evolutionary Neural Architecture Search for Remaining Useful Life Prediction.- ACOCaRS: Ant Colony Optimization Algorithm for Traveling Car Renter Problem.- A new type of anomaly detection problem in dynamic graphs: An ant colony optimization approach.- CSS- A Cheap-Surrogate-based Selection Operator for Multi-objective Optimization.- Empirical Similarity Measure for Metaheuristics.- Evaluation of Parallel Hierarchical Differential Evolution for Min-Max Optimization Problems Using SciPy.- Explaining Differential Evolution Performance Through Problem Landscape Characteristics.- Genetic improvement of TCP congestion avoidance.- Hybrid Acquisition Processes in Surrogate-based Optimization. Application to Covid-19 Contact Reduction.- Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System.- Modified Football Game Algorithm for Multimodal Optimization of Test TaskScheduling Problems Using Normalized Factor Random Key Encoding Scheme.- Performance Analysis of Selected Evolutionary Algorithms.- Refining Mutation Variants in Cartesian Genetic Programming.- Slime mould algorithm: An experimental study of nature-inspired optimizer.- SMOTE inspired extension for differential evolution.- The Influence of Local Search over Genetic Algorithms with Balanced Representations.- Trade-off of networks on weighted space analyzed via a method mimicking human walking track superposition.- Towards interpretable policies in multi-agent reinforcement learning tasksb.
| Erscheinungsdatum | 09.11.2022 |
|---|---|
| Reihe/Serie | Lecture Notes in Computer Science |
| Zusatzinfo | X, 277 p. 70 illus., 59 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 444 g |
| Themenwelt | Informatik ► Weitere Themen ► Hardware |
| Schlagworte | Applications • Artificial Intelligence • Computer Networks • computer programming • Computer Science • Computer systems • conference proceedings • Correlation Analysis • evolutionary algorithms • Genetic algorithms • graph theory • Informatics • machine learning • Optimization Problems • particle swarm optimization (PSO) • Research • Signal Processing • Swarm intelligence • theoretical computer science |
| ISBN-10 | 3-031-21093-X / 303121093X |
| ISBN-13 | 978-3-031-21093-8 / 9783031210938 |
| Zustand | Neuware |
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