Learning and Intelligent Optimization

Chapter 1 Evolutionary Dynamics of Extremal Optimization

Chapter 2 A Variable Neighborhood Descent Search Algorithm for Delay-Constrained Least-Cost Multicast Routing

Chapter 3 Expeditive Extensions of Evolutionary Bayesian Probabilistic Neural Networks

Chapter 4 New Bounds on the Clique Number of Graphs Based on Spectral Hypergraph Theory

Chapter 5 Beam-ACO Based on Stochastic Sampling: A Case Study on the TSP with Time Windows

Chapter 6 Flexible Stochastic Local Search for Haplotype Inference

Chapter 7 A Knowledge Discovery Approach to Understanding Relationships between Scheduling Problem Structure and Heuristic Performance

Chapter 8 Fitness Landscape Analysis for the Resource Constrained Project Scheduling Problem

Chapter 9 An ACO-Based Reactive Framework for Ant Colony Optimization: First Experiments on Constraint Satisfaction Problems

Chapter 10 Selection of Heuristics for the Job-Shop Scheduling Problem Based on the Prediction of Gaps in Machines

Chapter 11 Position-Guided Tabu Search Algorithm for the Graph Coloring Problem

Chapter 12 Corridor Selection and Fine Tuning for the Corridor Method

Chapter 13 Dynamic Multi-Armed Bandits and Extreme Value-Based Rewards for Adaptive Operator Selection in Evolutionary Algorithms

Chapter 14 Comparison of Coarsening Schemes for Multilevel Graph Partitioning

Chapter 15 Cooperative Strategies and Reactive Search: A Hybrid Model Proposal

Chapter 16 Study of the Influence of the Local Search Method in Memetic Algorithms for Large Scale Continuous Optimization Problems

Chapter 17 Neural Network Pairwise Interaction Fields for Protein Model Quality Assessment

Chapter 18 A Graph-Based Semi-supervised Algorithm for Protein Function Prediction from Interaction Maps

Chapter 19 Substitution Matrices and Mutual Information Approaches to Modeling Evolution