Intro
Preface
Organization
Contents
An Agent-Based Model to Investigate Different Behaviours in a Crowd Simulation
1 Introduction
2 The Mathematical Model
3 NetLogo Model
4 Experimental Results
5 Conclusions and Future Works
References
Accelerating Evolutionary Neural Architecture Search for Remaining Useful Life Prediction
1 Introduction
2 Background
3 Method
3.1 Multi-objective Optimization
3.2 Speeding up Evaluation
4 Experimental Setup
4.1 Computational Setup and Benchmark Dataset
4.2 Data Preparation and Training Details

5 Results
6 Conclusions
References
ACOCaRS: Ant Colony Optimization Algorithm for Traveling Car Renter Problem
1 Introduction
2 Related Work
3 Problem Description
4 ACOCaRS Algorithm
5 Experiment
5.1 Testbed
5.2 Results
6 Discussion
7 Conclusion and Future Work
References
A New Type of Anomaly Detection Problem in Dynamic Graphs: An Ant Colony Optimization Approach
1 Introduction
2 Anomaly Detection Problem
3 Proposed Approach
4 Numerical Experiments
4.1 Benchmarks
4.2 Parameter Setting
4.3 Anomaly Detection in Real-World Networks

5 Conclusion and Further Work
References
.28em plus .1em minus .1emCSS-A Cheap-Surrogate-Based Selection Operator for Multi-objective Optimization
1 Introduction
2 Background
2.1 Spherical Search
2.2 Cheap Surrogate Selection (CSS)
3 Proposed Method
3.1 General Framework of CSS-MOEA
3.2 The Detailed Process of CSS-MOEA
4 Experiment Results
5 Conclusion
References
Empirical Similarity Measure for Metaheuristics
1 Introduction
2 Related Works
3 Preliminaries
3.1 Metaheuristic Algorithms
3.2 Benchmark Functions
3.3 Parameter Tuning

4 Proposed Comparison Method
4.1 Algorithm Instances
4.2 Algorithm Profiling
4.3 Measuring Similarity
5 Results
5.1 Comparing Instances of the Same Algorithm
5.2 Comparing Instances of the Same Tuning Function
5.3 Clustering the Algorithms' Instances Based on Similarity
5.4 Discussion
6 Conclusion
References
Evaluation of Parallel Hierarchical Differential Evolution for Min-Max Optimization Problems Using SciPy
1 Introduction
2 Definition of the Problem
3 Differential Evolution for MinMax Problems
3.1 Overview of Differential Evolution

3.2 Hierarchical (Nested) Differential Evolution and Parallel Model
4 Experimental Setup and Results
4.1 Benchmark Test Functions
4.2 Parameter Settings
4.3 Results and Discussion
5 Conclusion and Future Work
References
Explaining Differential Evolution Performance Through Problem Landscape Characteristics
1 Introduction
2 Related Work
3 Experimental Setup
3.1 Benchmark Problem Portfolio
3.2 Landscape Data
3.3 Algorithm Portfolio
3.4 Performance Data
3.5 Regression Models
3.6 Leave-One Instance Out Validation
3.7 SHAP Explanations