Bioinspired Optimization Methods and Their Applications
Description
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.
More details
Other editions
Additional editions
Content
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.