Artificial Life and Evolutionary Computation

Name: Artificial Life and Evolutionary Computation | 17th Italian Workshop, WIVACE 2023, Venice, Italy, September 6-8, 2023, Revised Selected Papers
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17th Italian Workshop, WIVACE 2023, Venice, Italy, September 6-8, 2023, Revised Selected Papers

Marco Villani Stefano Cagnoni Roberto Serra(Editor)

Springer (Publisher)

Published on 29. March 2024

XII, 410 pages

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978-3-031-57430-6 (ISBN)

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Content

Intro
Preface
Organization
Contents
Algorithms for Complex Systems
Energy Consumption of Evolutionary Algorithms in JavaScript
1 Introduction
2 State of the Art
3 Experimental Results
4 Conclusions
References
A Tabu Search Algorithm for the Map Labeling Problem
1 Introduction
2 The Map Labeling Problem
3 The Tabu Search Algorithm
4 Methodology
4.1 Solution Format
4.2 Label Placements Score
4.3 Neighbor Function
5 Results
6 Conclusions and Future Work
References
How to Turn a Leaky Learner into a Sealed One
1 Introduction
2 Methods
2.1 Motivation
2.2 Rényi's Matrix-Based Entropy Functional
2.3 Information Bottleneck Principle
3 Experiments
3.1 Data
3.2 Model
3.3 Initialization Schemes
4 Results
4.1 Consistency
4.2 Learning Dynamics of Both Learners
5 Discussion
5.1 Numerical Issues
5.2 Assessment and Further Investigation
6 Conclusion
References
Capturing Emerging Complexity in Lenia
1 Introduction
2 Lenia
3 Methodology
3.1 Variation over Time (VoT) Fitness
3.2 Autoencoder (AE) Based Fitness
3.3 Auto Encoder Variation over Time
4 Experimental Setup and Results
5 Conclusions
References
On the Detection of Significant Pairwise Interactions in Complex Systems
1 Introduction
2 The zI Index
3 The New Approach
4 Results
5 Conclusions
Appendix A
References
Biologically Inspired Models
The Properties of Pseudo-Attractors in Random Boolean Networks
1 Introduction
2 Simulation Results
3 Evolved Networks
4 Conclusions
References
Analysing the Expressiveness of Metabolic Networks Representations
1 Background
2 Methods
2.1 KEGG as a Source of Metabolic Data
2.2 Abstract Metabolic Networks, Reaction Graphs and Metabolic DAGs
2.3 Graph Kernels
2.4 Data Visualisation and Analysis
3 Results and Discussion
3.1 Vertebrates Analysis
3.2 Mammals Analysis
3.3 Primates Analysis
4 Conclusion
References
scFBApy: A Python Framework for Super-Network Flux Balance Analysis
1 Introduction
1.1 State of the Art
1.2 Our Contribution
2 Material and Methods
2.1 Constraint-Based Modelling
2.2 From a Single-Network to a Super-Network
2.3 Transcriptomics-Derived Constraints to Metabolic Fluxes
2.4 Data Pre-processing
2.5 The ScFBApy Package
2.6 Datasets
2.7 The Metabolic Network Model
2.8 Experimental Setting
3 Experimental Results
3.1 Cooperation Between Cells Increases the Biomass Production
3.2 Cells Exchange Specific Metabolites to Increase the Biomass Production
3.3 Software Availability and Computational Architecture
4 Discussion and Conclusions
References
Semantic Information as a Measure of Synthetic Cells' Knowledge of the Environment
1 Introduction
2 Observed Semantic Information
3 Numerical Results
3.1 Evaluation of Viability and of Semantic Information
3.2 Normalization of pYt+1|Xt+1,Xt,Yt
4 Interpreting Semantic Information Values as a Measure of ``Knowledge'' SCs Have About Their Environment: A Preliminary Discussion
5 Conclusion
References
General Lines, Routes and Perspectives of Wetware Embodied AI. From Its Organizational Bases to a Glimpse on Social Chemical Robotics
1 Exorcizing the "Ghost" in the Machine
2 Wetware EAI
2.1 From EAI to "Organismically-Inspired Robotics", "Enactive AI" and Beyond: A Recap
2.2 Wetware EAI: The General Lines
2.3 Wetware Modeling of Life and Cognition
2.4 Autopoiesis and Autonomy
3 Social Robotics in the Chemical Domain
3.1 Social Robotics
3.2 Chemical Social Robotics
4 Concluding Remarks
References
A Proposed Mechanism for in vivo Programming Transmembrane Receptors
1 Introduction
2 Approach and Results
2.1 Relation Between Ligand Concentration and Number of Active Phosphorylation Sites
2.2 Application to G-Protein Coupled Receptors
3 Discussion
References
Complex Chemical Systems
Kauffman Model with Spatially Separated Ligation and Cleavage Reactions
1 Introduction
2 Extension of the Kauffmann Model
2.1 ``In-Out'' Processes
2.2 Cleavage and Ligation Processes
2.3 Consideration of Finite Energy Amounts
2.4 Diffusion Processes
3 Simulation Details
4 Computational Results
4.1 Revisiting the Original Kauffman Model Within One Container Only
4.2 Two Separate Containers
4.3 Two Containers with Diffusion
4.4 Comparison of Final Dynamics
5 Conclusion and Outlook
References
Percolation Breakdown in Binary and Ternary Monodisperse and Polydisperse Systems of Spherical Particles
1 Introduction
2 Simulation Details
3 Network Analysis and Percolation Theory
4 Computational Results
5 Summary and Outlook
References
Adaptation and Swarms
Entangled Gondolas. Design of Multi-layer Networks of Quantum-Driven Robotic Swarms
1 Introduction: The Core Idea
2 Novelties: Joining Swarm Robotics, Quantum Computing, and Multilayer Networks
2.1 A Quantum-Based Swarm of ``Telecommunicating'' Robots
2.2 Multilayer Networks to Model the Interactions Between Robots and Gondolas
2.3 Entanglement Between ``Gondolas''
3 Discussion and Conclusions
References
Generalizations of Evolved Decision-Making Mechanisms in Swarm Collective Perception
1 Introduction
2 Related Work
3 Methods
4 Results
5 Discussion
6 Conclusion
References
An Investigation of Graceful Degradation in Boolean Network Robots Subject to Online Adaptation
1 Introduction
2 Boolean Networks
3 Adaptation
4 Experimental Setting
5 Results
6 Conclusion
References
Blockchain-Empowered PSO for Scalable Swarm Robotics
1 Introduction
2 PSO in Swarm Robotics
3 Blockchain and the Tendermint System
4 Blockchain-Based PSO for Swarm Robotics
4.1 A Tendermint PSO Implementation
4.2 Asynchronous bPSO Implementation in Tendermint
4.3 Results and Evaluations
5 Conclusions
References
Hybrid GP/PSO Representation of 1-D Signals in an Autoencoder Fashion
1 Introduction
1.1 Genetic Programming and Latent Data Representations
1.2 Autoencoders
2 GP2SO: Symbolic Regression-Based Representation of Time-Dependent Signals
3 Implementation
3.1 Function and Terminal Set
3.2 Fitness Function
4 Proof of Concept
5 Possible Applications, Preliminary Tests, and Open Problems
6 Conclusions
References
Learning
Local Delay Plasticity Supports Generalized Learning in Spiking Neural Networks
1 Introduction
2 Delay Learning in Spiking Neural Networks
2.1 Activity-Dependent Delay Plasticity
2.2 Encoding and Decoding with Spike Times
3 Proof-of-concept: Classification of Handwritten Digits
3.1 Experimental Setup
3.2 Delay Training Improves Classification Accuracy
3.3 Networks with Plastic Delays Generalized Training to an Unseen Input Class
3.4 Output Activity Patterns Before and After Training
4 Discussion
4.1 SNNs Can Be Trained with Local Delay Plasticity
4.2 Delay Plasticity Enables Generalized Learning
4.3 Competition in the Output Layers to Improve Separability
4.4 Future Work: Mixed-Mode Learning and Neuromorphics
References
Improving PVC Detection in ECG Signals: A Recurrent Neural Network Approach
1 Introduction
2 Related Works
3 Methods
3.1 Dataset
3.2 Residual Neural Network
3.3 EGA
3.4 Map/Reduce Approach to Run EGA
3.5 Anomalies Detection
4 Results
5 Conclusions
References
Medicine
Clustering Trajectories to Study Diabetic Kidney Disease
1 Introduction
2 Methodology
2.1 A Shape-Similarity Clustering of Longitudinal Data
2.2 Category Theory for Trajectory Clustering
2.3 Study Population
3 Results of the Longitudinal Clustering
4 Discussion
References
Multi-classification of Alzheimer's Disease by NSGA-II Slices Optimization and Fusion Deep Learning
1 Introduction
2 Cohort Used. ADNI Database
3 Methodology
3.1 Phase 1: Selecting the Best Slices in the X and Y Plane
3.2 Phase 2: Fusion Several Deep Learning System with Different Slices Selected
4 Results
5 Conclusion
References
Exploiting the Potential of Bayesian Networks in Deriving New Insight into Diabetic Kidney Disease (DKD)
1 Introduction
2 Materials and Methods
2.1 The PROVALID Study
2.2 The Bayesian Networks
3 Results
4 Concluding Remarks
References
A Genetic Algorithm for Feature Selection for Alzheimer's Disease Detection Using a Deep Transfer Learning Approach
1 Introduction
2 Data Acquisition
2.1 The Tasks
2.2 Image Generation
3 The Proposed Workflow
3.1 Deep Feature Extraction
3.2 Feature Selection
3.3 Grid Search and Classification
3.4 Majority Vote
4 Experimental Results
5 Conclusions and Future Work
References
Social Systems
Learning Whether to be Informed in an Agent-Based Evolutionary Market Model
1 Introduction
2 The Model
3 Results
4 Conclusion
References
Heterogeneous Mean-Field Analysis of Best-of-n Decision Making in Networks with Zealots
1 Introduction
2 Method and Methodology
2.1 Model Description
2.2 A Mathematical Model with Option's Quality and Zealots
2.3 Equilibria and Stability of the Analytical Model
3 Results
4 Conclusion
References
Does `Community Detection' Find Real Emerging Meso-structures? A Statistical Test Based on Complex Networks Methods
1 Introduction
2 The Methodology
2.1 Focus on Within-Community Connections
2.2 Clarifications on W(G)
2.3 Generation of Null Networks as Term of Comparison
2.4 Test of Within-Community Connections
2.5 A Further Development: Controlling Random Tie Formation Based on Nodes' Heterogeneity
3 Analysis of UK Faculty Network
4 Conclusions
References
Self-loops in Social Networks: Behavior of Eigenvector Centrality
1 Introduction
2 A Brief Literature Survey
3 Experiments
3.1 Analyzing the Venetian Matrimonial Dataset
3.2 Freight Traffic Network
4 Conclusions
References
Evolutionary Story Sifting over the Log of a Social Simulation
1 Introduction
2 Previous Work
2.1 Social Simulations as Sources for Narrative Renderings
2.2 Story Sifting
2.3 Evolutionary Solutions for Exploring Search Spaces of Plot
3 Evolutionary Story Sifting from the Log of a Simulation
3.1 Capturing Plot Relevant Connections in the Representation Format
3.2 Evolutionary Content Selection Based on Plot Projections
3.3 Metrics on Story Draft Quality
4 Discussion
4.1 Comparative Evaluation
4.2 Relationship with Prior Work
5 Conclusions
References
Evolution of Attraction for Cooperation
1 Introduction
2 General Cooperation Modeling
3 Attraction Modeling
4 Basic Reference Experiments
5 Experiment 1: Network Evolution
6 Experiment 2: P Evolution
7 Experiment 3: Simultaneous Network and P Evolution
8 Experiment 4: General Strategies and Sizes
9 Discussion
References
Author Index

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Artificial Life and Evolutionary Computation

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