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

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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