Applications of Evolutionary Computation

Name: Applications of Evolutionary Computation | 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20-22, 2022, Proceedings
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25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20-22, 2022, Proceedings

Juan Luis Jiménez Laredo J. Ignacio Hidalgo Kehinde Oluwatoyin Babaagba(Herausgeber*in)

Springer (Verlag)

Erschienen am 15. April 2022

XVII, 756 Seiten

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Inhalt

Intro
Preface
Organization
Contents
Applications of Evolutionary Computation
An Enhanced Opposition-Based Evolutionary Feature Selection Approach
1 Introduction
2 Moth Flame Optimization
2.1 Binary Moth Flame Optimization
2.2 Binary Moth Flame Optimization for Feature Selection
3 The Proposed Approach
3.1 Initialization Using Opposition-Based Method
3.2 Retiring Flame
4 Experimental Setup and Results
5 Conclusions
References
A Methodology for Determining Ion Channels from Membrane Potential Neuronal Recordings
1 Introduction
2 Conductance-Based Model Description
3 Defining a Benchmark with Known Types of Ion Channels
4 Methodology and Experimental Setup
5 Experimental Results
6 Conclusions
A Mathematical Description of the Models
B Experimental Setup and Parameter Ranges
References
Swarm Optimised Few-View Binary Tomography
1 Introduction
2 Binary Tomographic Reconstruction
3 Swarm Optimisation
4 Constrained Search in High Dimensions
5 Reconstructions
6 Results
7 Discussion
8 Conclusions
References
Comparing Basin Hopping with Differential Evolution and Particle Swarm Optimization
1 Introduction
2 The Metaheuristics Studied
2.1 Basin Hopping
2.2 Differential Evolution
2.3 Particle Swarm Optimization
3 The Benchmarking Environment
4 Experimental Setup
5 Experimental Results
6 Conclusions
References
Combining the Properties of Random Forest with Grammatical Evolution to Construct Ensemble Models
1 Introduction
2 Methodology
2.1 Structured Grammatical Evolution
2.2 Random Structured Grammatical Evolution for Symbolic Regression Problems
3 Experimental Setup
3.1 Study Problems
3.2 Configuration of the Algorithms
4 Results
5 Conclusions
References
EvoCC: An Open-Source Classification-Based Nature-Inspired Optimization Clustering Framework in Python
1 Introduction
2 Related Works
3 Methodology
4 Framework Overview
4.1 Parameters
4.2 Datasets
4.3 Clustering with EvoCluster
4.4 Classification
4.5 Evaluation Measures
4.6 Results Management
5 Experiments and Visualizations
6 Conclusion and Future Works
References
Evolution of Acoustic Logic Gates in Granular Metamaterials
1 Introduction
2 Problem Statement
3 Simulation Setup
3.1 2D Granular Simulator
3.2 Optimization Method
4 Results and Discussion
4.1 Evolution of an Acoustic Band Gap
4.2 Evolving an AND Gate
4.3 Evolving an XOR Gate
5 Conclusion and Future Work
References
Public-Private Partnership: Evolutionary Algorithms as a Solution to Information Asymmetry
1 Introduction
2 The Problem
3 Proposed Approach
3.1 The Model
3.2 Data
3.3 Adversarial Optimization
3.4 Operator (EA1)
3.5 Public Administration (EA2)
4 Experimental Evaluation
4.1 Stochastic Optimization
4.2 Analysis
4.3 Real World Case
5 Conclusions and Future Work
References
The Asteroid Routing Problem: A Benchmark for Expensive Black-Box Permutation Optimization
1 Introduction
2 Background
2.1 Two-Body Problem
2.2 Maneuvers in Space
2.3 Lambert Problem
3 Asteroid Routing Problem
4 Optimization Algorithms
4.1 Sequential Least Squares Programming (SLSQP)
4.2 Greedy Nearest Neighbor Heuristic
4.3 Unbalanced Mallows Model (UMM)
4.4 Combinatorial Efficient Global Optimization (CEGO)
5 Experimental Study
5.1 Experimental Methodology
5.2 Results of the Black-Box Setting
5.3 Results of the Informed Setting
6 Conclusions
References
On the Difficulty of Evolving Permutation Codes
1 Introduction
2 Preliminaries
3 Incremental Construction with EA
3.1 Evolving Subsets of Permutations
3.2 Iterative Approach
3.3 Fitness Functions
4 Experimental Evaluation
4.1 Experimental Settings
4.2 Results
5 Conclusions and Future Work
References
Improving the Convergence and Diversity in Differential Evolution Through a Stock Market Criterion
1 Introduction
2 Background
2.1 Differential Evolution
2.2 Moving Average
2.3 Population Diversity
2.4 Opposition-Based Learning
3 Proposed Approach
4 Experiments and Results
4.1 Experiments over 30 Dimensions
4.2 Experiments over 50 Dimensions
5 Conclusions and Future Work
References
Search-Based Third-Party Library Migration at the Method-Level
1 Introduction
2 Background and Motivation
2.1 Background
2.2 Motivating Example
3 Search-Based API Migration
3.1 Solution Representation
3.2 Calculating the Fitness Function
3.3 Genetic Algorithm Operators and Parameters
4 Experimental Evaluation
4.1 Dataset Used
4.2 Metrics Used
4.3 Results
4.4 Discussion and Limitations
5 Related Work
6 Conclusion
References
Multi-objective Optimization of Extreme Learning Machine for Remaining Useful Life Prediction
1 Introduction
2 Background
3 Methods
3.1 Individual Encoding
3.2 Optimization Algorithms
4 Experimental Setup
4.1 Benchmark Dataset
4.2 Back-Propagation Neural Networks (BPNNs)
4.3 Computational Setup and Data Preparation
5 Experimental Results
6 Conclusions
References
Explainable Landscape Analysis in Automated Algorithm Performance Prediction
1 Introduction
2 Related Work
3 Automated Algorithm Performance Prediction
4 Experimental Setup
4.1 Data
4.2 Regression Models and Their Hyper-parameters
4.3 Evaluation
5 Results and Discussion
6 Conclusion
References
Search Trajectories Networks of Multiobjective Evolutionary Algorithms
1 Introduction
2 Related Work
3 Preliminaries
3.1 Search Trajectory Networks
3.2 Multiobjective Optimisation Problems
4 STN Extension for the Multiobjective Domain
5 Experiments
5.1 Experimental Parameters
5.2 Metrics
5.3 Reproducibility
6 Results
7 Conclusion
References
EvoMCS: Optimising Energy and Throughput of Mission Critical Services
1 Introduction
2 Related Work
3 EvoMCS: Multi-objective Optimization
3.1 Scenario and Technologies
3.2 Evolutionary Algorithm
3.3 Heuristic for Fitness
3.4 Selection Strategy
3.5 Operators to Generate Descendants
4 Experimentation
4.1 Validation Scenarios
4.2 Configuration Parameters
4.3 Evaluation Metrics
4.4 Profiles Validation - Inputs from EvoMCS
5 Results
5.1 Operators for the EvoMCS in H1(E/T)
5.2 Optimal Configurations
5.3 Optimal Profiles in Scenarios with Dense-Environments
6 Conclusions
References
RWS-L-SHADE: An Effective L-SHADE Algorithm Incorporation Roulette Wheel Selection Strategy for Numerical Optimisation
1 Introduction
2 Background
2.1 Differential Evolution
2.2 L-SHADE
3 RWS-L-SHADE
4 Experimental Results
5 Conclusions
References
WebGE: An Open-Source Tool for Symbolic Regression Using Grammatical Evolution
1 Introduction
2 Grammatical Evolution and Differential Evolution
3 Software Description
3.1 Modular Design
3.2 Parallel Execution
3.3 Persistence Layer
3.4 Implementation Technologies
4 WebGE Most Relevant Features
4.1 GUI for Experiments Management
4.2 Cross-fold Validation
4.3 Detailed Statistics
5 Use Case: Vladislavleva-4
6 Conclusions
References
A New Genetic Algorithm for Automated Spectral Pre-processing in Nutrient Assessment
1 Introduction
1.1 Goals
1.2 Organisation
2 Background and Related Work
2.1 Vibrational Spectroscopy
2.2 Partial Least Squares Regression
2.3 Spectral Pre-processing
2.4 PLSR for Nutrient Assessment
3 The Proposed Approach
3.1 Representations for the Two Populations for Co-evolution
3.2 Mapping of the Two Populations for Pairwise Evaluations
3.3 The Evaluation Method
4 Experiment Design
4.1 Datasets
4.2 Parameter Settings
5 Results and Discussions
5.1 Comparisons on the Training and Test Performance
5.2 Analyses on the Pre-processing Selection
5.3 Analyses on Feature Selection Results
6 Conclusions and Future Work
References
Evolutionary Computation in Edge, Fog, and Cloud Computing
Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling
1 Introduction
2 Related Work
3 Job Scheduling Structures
4 Structure Optimisation
4.1 Brute Force Search Algorithm
4.2 Genetic Algorithm
4.3 Simulated Annealing Algorithm
5 Simulation Experiments and Results
5.1 Setup
5.2 Experiment 1: Search Algorithm Comparison
5.3 Experiment 2: Server Processing Power Dispersion Impact
5.4 Experiment 3: Task Size Dispersion Impact
5.5 Experiment 4: Job Complexity Impact
6 Conclusion
References
Optimising Communication Overhead in Federated Learning Using NSGA-II
1 Introduction
2 Fundamental Concepts
2.1 Federated Learning
2.2 Communication Overhead in Distributed Deep Learning
3 Proposed Approach
3.1 The Proposed FL-COP Modelling and Formulation
3.2 The Communication-Overhead Reduction Routine
4 Experimental Study and Analysis
4.1 Problem Benchmarks and Experimental Settings
4.2 Experimental Results and Discussion
5 Conclusions and Perspectives
References
Evolutionary Machine Learning
Evolving Data Augmentation Strategies
1 Introduction
2 Related Work
3 Approach
3.1 Representation
3.2 Mutation
3.3 Evaluation
4 Experimental Setup
5 Experimental Results
6 Conclusions and Future Work
References
Inheritance vs. Expansion: Generalization Degree of Nearest Neighbor Rule in Continuous Space as Covering Operator of XCS
1 Introduction
2 XCS Classifier System
2.1 Classifier
2.2 Learning Flow
2.3 Covering
3 Local Covering
3.1 Overview
3.2 XCS-LCPCI
4 Proposed Method: Local Covering for Real Values
4.1 Procedure of Covering Classifier Generation
4.2 Nearest Neighbor Distance (NND)
4.3 Condition Generation Operation Based on Generalization Degree Expansion of Nearest Neighbor Rules
4.4 Condition Generation Operation Based on Generalization Degree Inheritance of Nearest Neighbor Rules
5 Experiment
5.1 Experiment 1: Real-Valued Multiplexer (RMUX) Problem
5.2 Experiment 2: Class-Imbalanced RMUX (IRMUX) Problem
5.3 Experiment 3: Paddy Leaf Classification Problem
5.4 Experimental Setup
6 Result
7 Discussion
8 Concluding Remarks
References
Detecting Nested Structures Through Evolutionary Multi-objective Clustering
1 Introduction
2 Background
2.1 MOCK, -MOCK, MOCLE and EMO-KC
3 An Improved Connectivity Index
4 Experimental Design
4.1 Datasets
4.2 Experimental Setup
4.3 Performance Assessment
5 Results and Discussion
5.1 The Impact of the con in the Optimization
6 Conclusion
References
Integrating Safety Guarantees into the Learning Classifier System XCS
1 Introduction
2 Related Work
3 XCS
4 Forbidden Classifiers
5 Experimental Results
5.1 6-Multiplexer
5.2 Woods1
5.3 Maze4
6 Conclusion
References
ANN-EMOA: Evolving Neural Networks Efficiently
1 Introduction
2 Neuroevolution
3 ANN-EMOA
3.1 Reduced Complexity in Variation
3.2 A Time-Invariant Encoding/Representation
3.3 Fostering Convergence and Diversity
3.4 Determining the Exact Es-Contribution Efficiently
3.5 Parameter Control
3.6 Procedure
4 Experimental Analysis
5 Summary and Outlook
References
Augmenting Novelty Search with a Surrogate Model to Engineer Meta-diversity in Ensembles of Classifiers
1 Introduction
2 Background
3 Methods and Materials
3.1 Neural Network Architectures
3.2 Diversity Metrics
3.3 Surrogate Model to Estimate Distances
3.4 Pretraining the Surrogate Model
3.5 Novelty Search Algorithm
3.6 Evaluation of Evolved Ensembles
3.7 Baseline: Previous Method
4 Experiments
4.1 Test Set 1: Resource Usage for Similar Complexity
4.2 Test Set 2: Expanding the Search Space
5 Results and Discussion
5.1 Hypothesis 1
5.2 Hypothesis 2
5.3 Hypothesis 3
5.4 Hypothesis 4
6 Conclusions and Future Work
References
Neuroevolution of Spiking Neural P Systems
1 Introduction
2 Background
3 Related Work
4 Method
4.1 NEAT
4.2 Genotype
4.3 Phenotype
4.4 Input Features
4.5 Fitness Evaluation
5 Results
5.1 Analysis of the Solutions
5.2 Comparison with State of the Art
6 Conclusions
References
Self-adaptation of Neuroevolution Algorithms Using Reinforcement Learning
1 Introduction
2 EXAMM
2.1 Opportunities for Improvement
3 Finite Action-Set Learning Automata
3.1 Action Probability Minimums
3.2 Negative Reinforcement
3.3 Proportional Reinforcement
3.4 Staged Learning
3.5 A Game of FALA
4 Applying FALA to EXAMM
4.1 Exploration vs Exploitation
5 Experimental Approach
6 Results
7 Conclusion
References
Soft Computing Applied to Games
Automating Speedrun Routing: Overview and Vision
1 Introduction
2 Speedruns and Categories
3 Related Work
3.1 Newman's Activity Categories
3.2 Scully-Blaker's Speedrun Categories
3.3 Routing
3.4 Criticism
4 Envisioned Models and Challenges
4.1 Weighted Game Event Digraph
4.2 Weighted Game State Graph
4.3 Vector Valued Game Graph
5 Prospective Solutions
6 Conclusions and Outlook
References
Co-evolution of Spies and Resistance Fighters
1 Introduction
2 Background
3 Rules of the Game
4 Strategies for Spies and Resistance Fighters
5 Co-evolution of Strategies
6 Evaluation
7 Conclusion
References
Deep Catan
1 Introduction
2 Background and Related Work
2.1 Monte Carlo Tree Search
2.2 Rules of Catan
2.3 Related Work
3 Deep Reinforcement Learning of Catan
3.1 Supervised Learning of the Value Network
3.2 Local Value Estimation
3.3 Expert Iteration of the Value Network
4 Experimental Results
4.1 Importance of the Budget
4.2 Training Performance
4.3 Evaluation of Neural Networks
5 Conclusion and Future Work
References
Machine Learning and AI in Digital Healthcare and Personalized Medicine
Vectorial GP for Alzheimer's Disease Prediction Through Handwriting Analysis
1 Introduction
2 Related Work
3 Materials and Methods
3.1 Vectorial Genetic Programming
3.2 Dataset
4 Experimental Results
4.1 Testing the Generalization Ability of VE_GP
4.2 Comparative Study
4.3 Models Evolved by VE_GP and Selected Features
5 Conclusions and Future Work
References
Negative Selection Algorithm for Alzheimer's Diagnosis: Design and Performance Evaluation
1 Introduction
2 Related Work
3 The Proposed Method
4 Experimental Results
4.1 Dataset
4.2 Features Selection
4.3 Implementation Details
4.4 Performance Evaluation
5 Conclusions
References
Evolutionary Computation in Image Analysis, Signal Processing and Pattern Recognition
Ground-Truth Segmentation of the Spinal Cord from 3T MR Images Using Evolutionary Computation
1 Introduction
2 Materials and Methods
2.1 Patients
2.2 MRI Acquisition
2.3 Data Preprocessing
2.4 Evolutionary Algorithm
2.5 Complexity
2.6 Computation Time
3 Results
3.1 Independency from Manual Outlining
3.2 Ground-Truth Value
4 Discussion
5 Conclusion
References
Applications of Nature-Inspired Computing for Sustainability and Development
A Machine Learning-Based Approach for Economics-Tailored Applications: The Spanish Case Study
1 Introduction
2 The Proposed Approach
2.1 Data Acquisition, Formatting and Pre-processing
2.2 Data-Clustering Pre-processing
2.3 Economic Profiling via Data Clustering
2.4 Economic Prediction
3 Experimental Results and Analysis
3.1 The *-CGAs' Variants Comparison
3.2 Economic Profiling
3.3 Economic Prediction
4 Conclusion
References
Multiobjective Electric Vehicle Charging Station Locations in a City Scale Area: Malaga Study Case
1 Introduction
2 The Multiobjective Electric Vehicle Charging Stations Location Problem
2.1 Mathematical Formulation
3 Algorithms
3.1 NSGA-2
3.2 SPEA-2
3.3 Main Operators
4 Experimental Setup
4.1 Problem Instance
4.2 Evaluated Metrics
4.3 Parameter Settings and Execution Platform
4.4 Baseline Method
5 Experimental Evaluation
5.1 Multiobjective Optimization Analysis
5.2 Computational Time Evaluation
5.3 Comparative Analysis
6 Conclusions and Future Work
References
Resilient Bio-inspired Algorithms
Brain Programming and Its Resilience Using a Real-World Database of a Snowy Plover Shorebird
1 Visual Attention
1.1 Artificial Dorsal Stream
1.2 Feature Integration
2 Brain Programming
3 Adversarial Attacks
3.1 Fast Gradient Sign Method
3.2 Adversarial Patch
3.3 Multipixel Attack
3.4 Noise
4 Experiments and Results
4.1 Image Database
4.2 Adversarial Example Generation
4.3 Results
5 Conclusions and Discussion
References
Resilient Bioinspired Algorithms: A Computer System Design Perspective
1 Introduction
2 Background
2.1 What Is Resilience?
2.2 Resilience from an Engineering Perspective
3 Bioinspired Algorithms as Resilient Systems
3.1 Integrity and Safety
3.2 Evolvability and Adaptability
3.3 Performability, Recoverability and Robustness
3.4 Reliability and Availability
3.5 Sustainability
4 Outlook and Challenges
References
Evolutionary Robotics
Seeking Specialization Through Novelty in Distributed Online Collective Robotics
1 Introduction
1.1 Objectives
2 Methods
2.1 The (,1)-Online Embodied EA
2.2 Seeking New Behaviors
3 Experiments
3.1 Simulation
3.2 Measures
4 Results and Discussion
4.1 Increasing the Pressure from the Environment
5 Conclusions
References
Open-Ended Search for Environments and Adapted Agents Using MAP-Elites
1 Introduction
2 Methods
2.1 Simulation Environment
2.2 Environment Encoding
2.3 Agent Encoding
2.4 Environment-Agent MAP-Elites
2.5 Behaviour Dimensions
3 Recording Data
3.1 Reference Maps
3.2 Found and Solved Environments
3.3 Environment Difficulty
4 Results
4.1 Reference Maps and Performance
4.2 Analysis of Found Environments
5 Discussion
6 Conclusion and Future Work
References
Out of Time: On the Constrains that Evolution in Hardware Faces When Evolving Modular Robots
1 Introduction
2 Related Work
3 Materials and Methods
3.1 Emerge Modules
3.2 Algorithms
3.3 Wall Time Calculation
4 Experimental Setup
5 Results
6 Discussion
7 Conclusions and Future Work
References
Analysis of Evolutionary Computation Methods: Theory, Empirics, and Real-world Applications
Neuroevolution Trajectory Networks of the Behaviour Space
1 Introduction
2 Related Work
3 Experimental Methodology
3.1 NEAT Variants
3.2 Parameters
4 Results
4.1 Analysis Setup
4.2 Performance Analysis
4.3 Behavioural Diversity Analysis
5 Neuroevolution Trajectory Networks
5.1 Sampling and Model Construction
5.2 Results and Discussion
6 Conclusion and Future Work
References
Parameter Tuning for the (1 + (, )) Genetic Algorithm Using Landscape Analysis and Machine Learning
1 Introduction
2 Preliminaries
2.1 (1+(,)) Genetic Algorithm
2.2 Benchmark Problems
2.3 Collecting Training Data
2.4 Training the Neural Network
3 Experiments
3.1 W-Model Problem
3.2 Linear Integer Weights Problem
3.3 MAX-3SAT Problem
4 Conclusion and Future Work
References
Towards a Principled Learning Rate Adaptation for Natural Evolution Strategies
1 Introduction
2 xNES
3 Learning Rate Adaptation
3.1 Motivation
3.2 Evolution Path for Covariance Matrix
3.3 Updating Learning Rate
3.4 Approximation of E["026B30D I(t)12 (t+1) "026B30D 2]12
3.5 Overall Procedure
4 Experiments
4.1 Experimental Setups
4.2 Evolution Path with Fixed Learning Rate
4.3 Behavior of Learning Rate Adaptation
4.4 Fixed Learning Rate vs. Adaptative Learning Rate
5 Conclusion
References
Convergence of Anisotropic Consensus-Based Optimization in Mean-Field Law
1 Introduction
2 Global Convergence in Mean-Field Law
2.1 Definition of Weak Solutions and Well-Posedness
2.2 Main Results
3 Proof of Theorem 2
3.1 Proof Sketch
3.2 Evolution of the Mean-Field Limit
3.3 Quantitative Laplace Principle
3.4 A Lower Bound for the Probability Mass Around v*
3.5 Proof of Theorem 2
4 A Machine Learning Example
5 Conclusion
References
Author Index

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