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• On Monte Carlo Tree Search for Weighted Vertex Coloring.- A RNN-based Hyper-heuristic for combinatorial problems.- Algorithm Selection for the Team Orienteering Problem.- Performance evaluation of a parallel ant colony optimization for the real-time train routing selection problem in large instances.- Deep Infeasibility Exploration Method for Vehicle Routing Problems.- Evolutionary Algorithms for the Constrained Two-Level Role Mining Problem.- Simplifying Dispatching Rules in Genetic Programming for Dynamic Job Shop Scheduling.- Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems.- A Beam Search for the Shortest Common Supersequence Problem Guided by an Approximate Expected Length Calculation.- Modeling the Costas Array Problem in QUBO for Quantum Annealing.- Penalty Weights in QUBO formulations: Permutation Problems.- PUBOi: a tunable benchmark with variable importance.- Stagnation Detection meets Fast Mutation.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 22nd European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2022, held as part of Evo*2022, in Madrid, Spain, during April 20-21, 2022, co-located with the Evo*2022 events: EvoMUSART, EvoApplications, and EuroGP. The 13 revised full papers presented in this book were carefully reviewed and selected from 28 submissions. They present recent theoretical and experimental advances in combinatorial optimization, evolutionary algorithms, and related research fields.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• Long Talks
• SonOpt: Sonifying Bi-objective Population-Based Optimization Algorithms
• 1 Introduction
• 2 Related Work
• 3 Methodology and System Overview
• 4 Experimental Study
• 4.1 Experimental Setup
• 4.2 Experimental Analysis
• 5 Conclusion and Future Work
• References
• A Systematic Evaluation of GPT-2-Based Music Generation
• 1 Introduction
• 2 Background
• 2.1 GPT-2 Models
• 2.2 Representing Music Data for GPT-2 Input
• 2.3 Statistical Analysis of Generative Music Models
• 3 Musical Metrics
• 4 Dataset Curation

• 5 Evaluating Generative Model Output
• 5.1 Varying the Training Level
• 5.2 Varying the Training Corpus
• 6 Web Application
• 7 Conclusions and Future Work
• References
• Expressive Aliens
• Laban Effort Factors for Non-anthropomorphic Morphologies
• 1 Introduction
• 2 Background
• 2.1 Movement Qualities
• 2.2 Motion Synthesis
• 2.3 Anthropomorphic versus Non-anthropomorphic Characters
• 2.4 Physical Validity versus Expressivity
• 3 Implementation
• 3.1 Morphologies
• 3.2 SAC Algorithm
• 3.3 Observation Vector
• 3.4 Rewards
• 4 Training
• 5 Results
• 6 Discussion

• 7 Conclusion and Outlook
• References
• Painting with Evolutionary Algorithms
• 1 Introduction
• 2 Rearranging Brush Strokes
• 3 Algorithms
• 4 Experiment and Results
• 5 Extrapolation
• 6 Some Final Remarks
• References
• Evolutionary Construction of Stories that Combine Several Plot Lines
• 1 Introduction
• 2 Related Work
• 2.1 Plot Line Combination
• 2.2 Computational Metrics for Stories
• 2.3 Evolutionary Construction of Narratives
• 3 An Evolutionary Multiplot Story Composer
• 3.1 The Knowledge Resources
• 3.2 Character Fusion and Discourse Planning

• 3.3 Representing Multiplot Stories for Evolutionary Construction
• 3.4 Constructing an Initial Population
• 3.5 Evolutionary Operators
• 3.6 Fitness Functions
• 4 Discussion
• 4.1 Results
• 4.2 Relation with Previous Work
• 5 Conclusions
• References
• Fashion Style Generation: Evolutionary Search with Gaussian Mixture Models in the Latent Space
• 1 Introduction
• 2 Related Work
• 2.1 GANs
• 2.2 Fashion Styles
• 2.3 Evolutionary Search of GANs' Latent Space
• 3 Dataset
• 4 Model
• 4.1 Generative Model
• 4.2 Style Model
• 4.3 Evolutionary Search
• 5 Results
• 6 Discussion

• 7 Conclusion and Future Work
• References
• Classification of Guitar Effects and Extraction of Their Parameter Settings from Instrument Mixes Using Convolutional Neural Networks
• 1 Introduction
• 2 Method and Materials
• 2.1 Dataset for Guitar Effect Parameter Extraction
• 2.2 Dataset for Guitar Effect Classification
• 2.3 Time-Frequency Representations
• 2.4 Convolutional Neural Networks
• 2.5 Training and Evaluation
• 2.6 Baseline
• 2.7 Robustness Analysis
• 3 Results
• 3.1 Effect Classification
• 3.2 Effect Parameter Extraction
• 3.3 Robustness to Noise and Pitch Shifts

This book constitutes the refereed proceedings of the 10th European Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2022, held as part of Evo* 2022, in April 2022, co-located with the Evo* 2022 events, EvoCOP, EvoApplications, and EuroGP. The 20 full papers and 6 short papers presented in this book were carefully reviewed and selected from 66 submissions. They cover a wide range of topics and application areas, including generative approaches to music and visual art, deep learning, and architecture.
(source: Nielsen Book Data)

• 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

This book constitutes the refereed proceedings of the 25th International Conference on Applications of Evolutionary Computation, EvoApplications 2022, held as part of Evo*2022, in April 2022, co-located with the Evo*2022 events EuroGP, EvoCOP, and EvoMUSART. The 46 revised full papers presented in this book were carefully reviewed and selected from 67 submissions. .
(source: Nielsen Book Data)

• Long Presentations.- Evolving Adaptive Neural Network Optimizers for Image Classification.- Combining Geometric Semantic GP with Gradient-descent Optimization.- One-Shot Learning of Ensembles of Temporal Logic Formulas for Anomaly Detection in Cyber-Physical Systems.- Multi-objective GP with AWS for Symbolic Regression.- SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming.- Evolutionary Design of Reduced Precision Levodopa-Induced Dyskinesia Classifiers.- Using Denoising Autoencoder Genetic Programming to Control Exploration and Exploitation in Search.- Program Synthesis with Genetic Programming: The Influence of Batch Sizes.- Genetic Programming-Based Inverse Kinematics for Robotic Manipulators.- On the Schedule for Morphological Development of Evolved Modular Soft Robots.- An Investigation of Multitask Linear Genetic Programming for Dynamic Job Shop Scheduling.- Cooperative Co-Evolution and Adaptive Team Composition for a Multi-Rover Resources Allocation Problem.- Short Presentations.- Synthesizing Programs from Program Pieces using Genetic Programming and Refinement Type Checking.- Creating Diverse Ensembles for Classification with Genetic Programming and Neuro-MAP-Elites.- Evolving Monotone Conjunctions in Regimes Beyond Proved Convergence.- Accurate and Interpretable Representations of Environments with Anticipatory Learning Classifier Systems.- Exploiting Knowledge from Code to Guide Program Search.- Multi-Objective Genetic Programming for Explainable Reinforcement Learning.- Permutation-Invariant Representation of Neural Networks with Neuron Embeddings.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 25th European Conference on Genetic Programming, EuroGP 2022, held as part of Evo*2021, as Virtual Event, in April 2022, co-located with the Evo*2022 events, EvoCOP, EvoMUSART, and EvoApplications. The 12 revised full papers and 7 short papers presented in this book were carefully reviewed and selected from 35 submissions. The wide range of topics in this volume reflects the current state of research in the field. The collection of papers cover topics including developing new operators for variants of GP algorithms, as well as exploring GP applications to the optimization of machine learning methods and the evolution of complex combinational logic circuits. .
(source: Nielsen Book Data)