Bioinspired Optimization Methods and Their Applications. 10th International Conference, BIOMA 2022 Maribor, Slovenia, November 17–18, 2022 Proceedings

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
	4 Results and Discussion
		4.1 Optimization Algorithms Performance
		4.2 Performance Prediction
		4.3 Linking ELA Features to DE Performance
	5 Conclusions
	References
Genetic Improvement of TCP Congestion Avoidance
	1 Introduction
	2 Background
	3 Related Works
	4 Method
		4.1 Code Simplification Procedure
	5 Experimental Results
	6 Conclusions and Future Work
	References
Hybrid Acquisition Processes in Surrogate-Based Optimization. Application to Covid-19 Contact Reduction
	1 Introduction
	2 Background on Surrogate-Based Optimization
	3 COVID-19 Contact Reduction Problem
	4 Hybrid Acquisition Processes
	5 Experiments
	6 Conclusion
	References
Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System
	1 Introduction
	2 Related Work
	3 The Supervised Rule-Based Learning System
	4 Evaluation
		4.1 Experiment Design
		4.2 Results
	5 Conclusion
	References
Modified Football Game Algorithm for Multimodal Optimization of Test Task Scheduling Problems Using Normalized Factor Random Key Encoding Scheme
	1 Introduction
	2 Problem Description and Mathematical Modeling
	3 The Proposed Modified Football Game Algorithm (mFGA)
		3.1 Classic FGA
		3.2 Modified FGA
	4 Normalized Factor Random Key Encoding Scheme
	5 Multimodal Single-Objective Optimization of TTSP
	6 Comparison and Discussion
	7 Conclusion and Future Works
	References
Performance Analysis of Selected Evolutionary Algorithms on Different Benchmark Functions
	1 Introduction
	2 Related Work
	3 Experiment
		3.1 CEC 2022 Single Objective Bound Constrained Numerical Optimization
		3.2 CEC 2021 Single Objective Bound Constrained Optimization
		3.3 CEC 2017 Single Objective Bound Constrained Optimization
	4 Discussion
	5 Conclusion
	References
Refining Mutation Variants in Cartesian Genetic Programming
	1 Introduction
	2 Related Work
	3 Cartesian Genetic Programming
		3.1 Introduction to Cartesian Genetic Programming
		3.2 Mutation Algorithm
	4 Further Changes in the Mutation Algorithm
		4.1 Probabilistic Mutation
		4.2 Single and Multiple Mutation
	5 Preliminaries
		5.1 Experiment Description
		5.2 Datasets
	6 Experiments
		6.1 Impact of Different Probabilistic Mutation Strategies
		6.2 Impact of Multi-n and DMulti-n
	7 Conclusion
	References
Slime Mould Algorithm: An Experimental Study of Nature-Inspired Optimiser
	1 Introduction
		1.1 Slime Mould Algorithm
		1.2 Previous Works
	2 Newly Proposed Variants of SMA
		2.1 Linear Reduction of the Population Size
		2.2 Eigen Transformation
		2.3 Perturbation
		2.4 Adaptation of Parameter z
	3 Methods Used in Experiments
	4 Experimental Settings
	5 Results
	6 Conclusion
	References
SMOTE Inspired Extension for Differential Evolution
	1 Introduction
	2 Background
		2.1 Differential Evolution
		2.2 Synthetic Minority Oversampling Technique (SMOTE)
		2.3 Literature Overview
	3 Proposed Mechanism for Differential Evolution
	4 Experimental Analysis
		4.1 Setup
		4.2 Comparison Against Other Mechanisms
		4.3 Incorporation into Improved Algorithm Variants
	5 Conclusion
	References
The Influence of Local Search on Genetic Algorithms with Balanced Representations
	1 Introduction
	2 Background
		2.1 Balanced Crossover Operators
		2.2 Boolean Functions
	3 Local Search of Boolean Functions
	4 Experiments
		4.1 Experimental Setting
		4.2 Results
		4.3 Discussion
	5 Conclusions
	References
Trade-Off of Networks on Weighted Space Analyzed via a Method Mimicking Human Walking Track Superposition
	1 Introduction and Related Work
	2 Simulation Model of WTSN on Weighted Space
		2.1 Generation Process of WTSN on a Mixture of Different Ground Conditions
		2.2 Pareto-Optimal Path Between Two Demand Vertices
		2.3 Algorithm for WTSN on Weighted Space
	3 Analysis of Differences in Pareto Frontier by Weighted Space
		3.1 Experimental Spaces Setting
		3.2 Result of Pareto Frontier Approximation
	4 Discussion
	5 Conclusion and Further Work
	References
Towards Interpretable Policies in Multi-agent Reinforcement Learning Tasks
	1 Introduction
	2 Related Work
	3 Method
		3.1 Creation of the Teams
		3.2 Fitness Evaluation
		3.3 Individual Encoding
		3.4 Operators
	4 Experimental Setup
		4.1 Environment
		4.2 Parameters
	5 Experimental Results
		5.1 Interpretation
		5.2 Comparison with a Non Co-Evolutionary Approach
	6 Conclusions and Future Works
	References
Author Index