
Applications of Evolutionary Computation
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The 46 revised full papers presented in this book were carefully reviewed and selected from 67 submissions.
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Content
- 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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