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• Applications of Evolutionary Computation
• Analysis of Evolutionary Computation Methods: Theory, Empirics, and Real-World Applications
• Computational Intelligence for Sustainability
• Evolutionary Computation in Edge, Fog, and Cloud Computing
• Evolutionary Machine Learning
• Machine Learning and AI in Digital Healthcare and Personalized Medicine
• Resilient Bio-Inspired Algorithms
• Soft Computing applied to Games
• Surrogate-Assisted Evolutionary Optimisation.

This book constitutes the refereed proceedings of the 25th International Conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of Evo*2023, in April 2023, co-located with the Evo*2023 events EuroGP, EvoCOP, and EvoMUSART. The EuroGP focused on the technique of genetic programming, EvoCOP targeted evolutionary computation in combinatorial optimization, and EvoMUSART was dedicated to evolved and bio-inspired music, sound, art, and design. The EvoApplications 2023 presents papers on the different areas: Analysis of Evolutionary Computation Methods: Theory, Empirics, and Real-World Applications, Applications of Bio-inspired Techniques on Social Networks, Evolutionary Computation in Edge, Fog, and Cloud Computing, Evolutionary Computation in Image Analysis, Signal Processing, and Pattern Recognition and others.

• Long Talks
• LooperGP: A Loopable Sequence Model for Live Coding Performance using GuitarPro Tablature
• Chordal embeddings based on topology of the tonal space
• Music Generation with Multiple Ant Colonies Interacting on Multilayer Graphs
• Automatically Adding to Artistic Cultures
• Extending Generative Neo-Riemannian Theory for Event-based Soundtrack Production
• Is beauty in the age of the beholder
• Extending the Visual Arts experience: Sonifying Paintings with AI
• Application of Neural Architecture Search to Instrument Recognition in Polyphonic Audio
• AI-rmonies of the Spheres
• SUNMASK: Mask Enhanced Control in Step Unrolled Denoising Autoencoders
• SketchSynth: cross-modal control of sound synthesis
• Towards the Evolution of Prompts with MetaPrompter
• Is Writing Prompts Really Making Art
• Using GPT-3 to achieve semantically relevant data sonificiation for an art installation
• Using Autoencoders to Generate Skeleton-based Typography
• Visual Representation of the Internet Consumption in the European Union
• GTR-CTRL: Instrument and Genre Conditioning for Guitar-Focused Music Generation with Transformers
• Artistic Curve Steganography Carried by Musical Audio
• LyricJam Sonic: A Generative System for Real-Time Composition and Musical Improvisation
• Searching For Human Bias Against AI-Composed Music
• Short Talks
• Fabric Sketch Augmentation & Styling via Deep Learning & Image Synthesis
• Transposition of Simple Waveforms from Raw Audio with Deep Learning
• AI-aided Ceramic Sculptures: Bridging Deep Learning with Materiality
• OSC-Qasm: Interfacing Music Software with Quantum Computing
• EvoDesigner: Aiding the exploration of innovative graphic design solutions
• Improving Automatic Music Genre Classification Systems by Using Descriptive Statistical Features of Audio Signals
• Musical Genre Recognition based on Deep Descriptors of Harmony, Instrumentation, and Segments.

This book constitutes the refereed proceedings of the 12th European Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2023, held as part of Evo* 2023, in April 2023, co-located with the Evo* 2023 events, EvoCOP, EvoApplications, and EuroGP. The 20 full papers and 7 short papers presented in this book were carefully reviewed and selected from 55 submissions. They cover a wide range of topics and application areas of artificial intelligence, including generative approaches to music and visual art, deep learning, and architecture.

• Networks and Complex systems
• Collaborative learning over Cellular Automata
• An agent-based model for crowd simulation
• Chemical Neural Networks and Semantic Information investigated through Synthetic Cells
• Spread of perturbations in supply chain networks: the effect of the bow-tie organization on the resilience of the global automotive system
• An efficient implementation of Flux Variability Analysis for metabolic networks
• Exploring the solution space of cancer evolution inference frameworks for single-cell sequencing data
• Computational investigation of the clustering of droplets in widening pipe geometries
• Network Creation During Agglomeration Processes of Polydisperse and Monodisperse Systems of Droplets
• Artificial Chemistry Performed in an Agglomeration of Droplets with Restricted Molecule Transfer
• Modelling wet-dry cycles in the binary polymer model
• On the growth of chemical diversity
• Kernel-based early fusion of structure and attribute information for detecting communities in attributed networks
• Cultural Innovation Triggers Inequality in a Sharing Economy
• Metaheuristics, robotics, and machine learning
• Online adaptation of robots controlled by nanowire networks: A preliminary study
• The role of dynamical regimes of online adaptive BN-robots in noisy environments
• A novel evolutionary approach for Neural Architecture Search
• Single and Multi-objective Genetic Programming Methods for Prediction Intervals
• Green design of Single Frequency Networks by Multiband Robustness and a hybrid metaheuristic
• WanDa: A Mobile Application to Prevent Wandering
• Magnetic Devices Behavioral Modeling based on Genetic Programming and Neural Networks
• Evolutionary Stitching of Plot Units with Character Threads
• Impact of Morphology Variations on Evolved Neural Controllers for Modular Robots
• EGSGP: an Ensemble System Based on Geometric Semantic Genetic Programming
• Real-time monitoring tool for SNN hardware architecture.

This book constitutes the refereed proceedings of the 16th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2022, held in Gaeta, Italy, during September 14-16, 2022. The 21 full papers and 3 short papers included in this book were carefully reviewed and selected from 45 submissions. They were organized in topical sections as follows: answer set programming; networks and complex systems, metaheuristics, robotics, and machine learning.

• Evolutionary Computation & Swarm Intelligence
• Surrogate Model-Assisted Evolutionary Algorithms for Parameter Identification of Electrochemical Model of Lithium-Ion Battery: A Comparison Study
• Research on Multi-modal Multi-Objective Path Planning by Improved Ant Colony Algorithm
• Local Path Planning Algorithm Designed for Unmanned Surface Vessel Based on Improved Genetic Algorithm
• A Multi-Objective Particle Swarm Algorithm Based on a Preference Strategy
• An Improved Harris Hawk Optimization Algorithm Based on Spiral Search and Neighborhood Perturbation
• S-Plane Controller Parameter Tuning Based on IAFSA for UUV
• A Reinforcement-Learning-Driven Bees Algorithm for Large-Scale Earth Observation Satellite Scheduling
• Optimal Formation of UUV Groups Based on Shape Theory and Improved Ant Colony Algorithm Under Communication Delay
• Global Path Planning for Unmanned Ships Based on Improved Particle Swarm Algorithm
• A Self-Adaptive Single-objective Multitasking Optimization Algorithm
• Improved Whale Optimization Algorithm by Multi-mechanism Fusion
• Multi-stage Objective Function Optimized Hand-Eye Self-calibration of Robot in Autonomous Environment
• An Improved Chaos-based Particle Swarm Optimization Algorithm
• Task Location Distribution based Genetic Algorithm for UAV Mobile Crowd Sensing
• The Utilities of Evolutionary Multiobjective Optimization for Neural Architecture Search An Empirical Perspective
• Research on Unmanned Ship Collision Avoidance Algorithm based on Improved Particle Swarm Pptimization Algorithm
• Solving Constrained Multi-objective Optimization Problems with Passive Archiving Mechanism
• A Comparison of Large-scale MOEAs with Informed Initialization for Voltage Transformer Ratio Error Estimation
• Enabling Surrogate-assisted Evolutionary Reinforcement Learning via Policy Embedding
• An Improved MPCA Algorithm with Weight Matrix Based on Many-objective Optimization
• Machine Learning & Deep Learning
• Research on Helmet Wearing Detection Based on Improved YOLOv4 Algorithm
• Research on Target Detection Algorithm of Unmanned Surface Vehicle Based on Deep Learning
• A Review On Bio-inspired Fluid Mechanics Via Deep Reinforcement Learning
• Memristor-Based Neural Network Circuit of Operant Conditioning with Overshadowing
• Continual Learning with a Memory of Non-similar Samples
• Research on Soil Moisture Prediction Based on LSTM-Transformer Model
• Graph Contrastive Learning with Intrinsic Augmentations
• Research on Global Collision Avoidance Algorithm for Unmanned Ship Based on Improved Artificial Potential Field Algorithm
• OCET: One-dimensional Convolution Embedding Transformer for Stock Trend Prediction
• A Rapid, Precise Spiking Neural Network Approach for Image Recognition
• Research on Improved Image Dehazing Algorithm Based on Dark Channel Prior
• Reinforcement Learning Based Vertical Scaling for Hybrid Deployment in Cloud Computing
• Swing-up and Balance Control of Cart-pole Based on Reinforcement Learning DDPG
• Yolov5 Outdoor Dynamic Object Detection Based on Multi-scale Feature Fusion
• Intelligent Control & Simulation
• Multi-UUV Formation Cooperative Full-area Coverage Search Method
• Multi-scale Simulations of Flow in the Upper Open Area of a Luxury Cruise Ship
• Research on Course Control Algorithm of Unmanned Craft based on Model Predictive Control
• Multi-UUV Underwater Target Cooperative Detection Task Planning and Assignment
• Multi-UUV Cooperative Navigation and Positioning Algorithm under Communication Delay
• The Research of the Consistency Control under The Condition of Time-lag of Isomerism AUV Group Communication
• Non-uniform Deformation Behavior of Magneto-sensitive Elastomer Containing Uniform Sphere Particles with V Arrangement
• Study on the Influence of Exit Width Change on Heterogeneous Passengers Evacuation based on the Social Force Model
• A Review of Longitudinal Vibration and Vibration Reduction Technology of Propulsion Shafting
• Design and Simulation of Heading Controller for Unmanned Boat Based on Fuzzy Neural PID
• Flexible Formation Control of Multiple Unmanned Vehicles based on Artificial Potential Field Method
• Local Path Planning Combined with the Motion State of Dynamic Obstacles
• Simulation of Airflow Characteristics of a Seabird Following a Ship Based on Steady States
• Numerical Simulation of Bionic Undulating Fin Surface Drag Reduction Structure
• Molecular Computing & Nanotechnology
• Integrated Design and Absorbing Performance Analysis for Periodic Wave Absorbing and Bearing Structures
• Diffusion Optimization Algorithm Based on Membrane Calculation
• Tuning Geometric Conformations of Curved DNA Structures by Controlling Positions of Nicks
• A Sensitive Nanothermometer Based on DNA Triplex Structure
• Microwave Absorption Properties of Double-layer Absorbing Material Based on Carbonyl Iron and Graphene Composites
• Reconfigurable Nanobook Structure Driven by Polymerase-triggered DNA Strand Displacement
• An Analysis for Thermal Conductivity of Graphene/polymer Nanocomposites
• Simulation of Aflatoxin B1 Detection Model Based on Hybridization Chain Reaction.

This book constitutes the refereed proceedings of the 17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022, held in Wuhan, China, during December 1618, 2022. The 56 full papers included in this book were carefully reviewed and selected from 148 submissions. They were organized in topical sections as follows: evolutionary computation and swarm intelligence; machine learning and deep learning; intelligent control and simulation and molecular computing and nanotechnology.

• Computational Models of Evolution and Development
• Analysis of Gene Regulatory Networks
• Evolutionary Synthesis of Gene Regulatory Dynamics
• Evolution of Morphological Development
• Evolution of Neural Development
• Computational Brain-Body Co-Evolution
• Evolutionary Morphogenetic Self-Organization of Swarm Robots
• Towards Evolutionary Developmental Systems

This book provides a basic yet unified overview of theory and methodologies for evolutionary developmental systems. Based on the authors extensive research into the synergies between various approaches to artificial intelligence including evolutionary computation, artificial neural networks, and systems biology, it also examines the inherent links between biological intelligence and artificial intelligence. The book begins with an introduction to computational algorithms used to understand and simulate biological evolution and development, including evolutionary algorithms, gene regulatory network models, multi-cellular models for neural and morphological development, and computational models of neural plasticity. Chap. 2 discusses important properties of biological gene regulatory systems, including network motifs, network connectivity, robustness and evolvability. Going a step further, Chap. 3 presents methods for synthesizing regulatory motifs from scratch and creating more complex regulatory dynamics by combining basic regulatory motifs using evolutionary algorithms. Multi-cellular growth models, which can be used to simulate either neural or morphological development, are presented in Chapters 4 and 5. Chap. 6 examines the synergies and coupling between neural and morphological evolution and development. In turn, Chap. 7 provides preliminary yet promising examples of how evolutionary developmental systems can help in self-organized pattern generation, referred to as morphogenetic self-organization, highlighting the great potentials of evolutionary developmental systems. Finally, Chap. 8 rounds out the book, stressing the importance and promise of the evolutionary developmental approach to artificial intelligence. Featuring a wealth of diagrams, graphs and charts to aid in comprehension, this book offers a valuable asset for graduate students, researchers and practitioners who are interested in pursuing a different approach to artificial intelligence

• Fairer comparisons for Travelling Salesman Problem solutions using Hash Functions
• Application of Adapt-CMSA to the Two-Echelon Electric Vehicle Routing Problem with Simultaneous Pickup and Deliveries
• Real-World Vehicle Routing using Adaptive Large Neighborhood Search
• A Multilevel Optimization Approach for Large Scale Battery Exchange Station Location Planning
• A memetic algorithm for deinterleaving pulse trains
• Application of Negative Learning Ant Colony Optimization to the Far From Most String Problem
• Monte Carlo Tree Search with Adaptive Simulation: a Case Study on Weighted Vertex Coloring
• Evolutionary Strategies for the Design of Binary Linear Codes
• A Policy-Based Learning Beam Search for Combinatorial Optimization
• Cooperative Coevolutionary Genetic Programming Hyper Heuristic for Budget Constrained Dynamic Multi-workflow Scheduling in Cloud Computing
• OneMax is not the Easiest Function for Fitness Improvements
• The Cost of Randomness in Evolutionary Algorithms: Crossover Can Save Random Bits
• Multi-objectivization Relaxes Multi-funnel Structures in Singleobjective NK-landscapes
• Decision/Objective Space Trajectory Networks for Multi-objective Combinatorial Optimisation
• On the e_ect of solution representation and neighborhood definition in AutoML Fitness Landscapes.

This book constitutes the refereed proceedings of the 23rd European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2023, held as part of Evo*2023, in Brno, Czech Republic in April 2023, co-located with the Evo*2023 events: EvoMUSART, EvoApplications, and EuroGP. The 15 revised full papers presented in this book were carefully reviewed and selected from 32 submissions. They present recent theoretical and experimental advances in combinatorial optimization, evolutionary algorithms, and related research fields.

• Intro
• Preface
• Contents
• Part I Background
• 1 Introduction
• 1.1 Optimization
• 1.2 Evolutionary Optimization
• 1.3 Evolutionary Multi-Task Optimization
• 1.4 Organization of the Book
• 2 Overview and Application-Driven Motivations of Evolutionary Multitasking
• 2.1 An Overview of EMT Algorithms
• 2.2 EMT in Real-World Problems
• 2.2.1 Category 1: EMT in Data Science Pipelines
• 2.2.2 Category 2: EMT in Evolving Embodied Intelligence
• 2.2.3 Category 3: EMT in Unmanned Systems Planning
• 2.2.4 Category 4: EMT in Complex Design

• 2.2.5 Category 5: EMT in Manufacturing, Operations Research
• 2.2.6 Category 6: EMT in Software and Services Computing
• Part II Evolutionary Multi-Task Optimization for Solving Continuous Optimization Problems
• 3 The Multi-Factorial Evolutionary Algorithm
• 3.1 Algorithm Design and Details
• 3.1.1 Multi-Factorial Optimization
• 3.1.2 Similarity and Difference Between Multi-factorial Optimization and Multi-Objective Optimization
• 3.1.3 The Multi-Factorial Evolutionary Algorithm
• 3.1.3.1 Population Initialization
• 3.1.3.2 Genetic Mechanisms
• 3.1.3.3 Selective Evaluation

• 3.1.3.4 Selection Operation
• 3.1.3.5 Summarizing the Salient Features of the MFEA
• 3.2 Empirical Study
• 3.2.1 Multitasking Across Functions with Intersecting Optima
• 3.2.2 Multitasking Across Functions with Separated Optima
• 3.2.3 Discussions
• 3.3 Summary
• 4 Multi-Factorial Evolutionary Algorithm with Adaptive Knowledge Transfer
• 4.1 Algorithm Design and Details
• 4.1.1 Representative Crossover Operators for Continuous Optimization
• 4.1.2 Knowledge Transfer via Different Crossover Operators in MFEA
• 4.1.3 MFEA with Adaptive Knowledge Transfer

• 4.1.3.1 Adaptive Assortative Mating and Adaptive Vertical Cultural Transmission
• 4.1.3.2 Adaptation of Transfer Crossover Indicators
• 4.2 Empirical Study
• 4.2.1 Experimental Setup
• 4.2.2 Performance Metric
• 4.2.3 Results and Discussions
• 4.2.3.1 Common Multi-Task Benchmarks
• 4.2.3.2 Complex Multi-Task Problems
• 4.2.4 Other Issues
• 4.3 Summary
• 5 Explicit Evolutionary Multi-Task Optimization Algorithm
• 5.1 Algorithm Design and Details
• 5.1.1 Denoising Autoencoder
• 5.1.2 The Explicit EMT Paradigm
• 5.1.2.1 Learning of Task Mapping

• 5.1.2.2 Explicit Genetic Transfer Across Tasks
• 5.2 Empirical Study
• 5.2.1 Single-Objective Multi-Task Optimization
• 5.2.1.1 Experiment Setup
• 5.2.1.2 Results and Discussions
• 5.2.2 Multi-Objective Multi-Task Optimization
• 5.2.2.1 Experiment Setup
• 5.2.2.2 Results and Discussions
• 5.3 Summary
• Part III Evolutionary Multi-Task Optimization for Solving Combinatorial Optimization Problems
• 6 Evolutionary Multi-Task Optimization for Generalized Vehicle Routing Problem with Occasional Drivers

A remarkable facet of the human brain is its ability to manage multiple tasks with apparent simultaneity. Knowledge learned from one task can then be used to enhance problem-solving in other related tasks. In machine learning, the idea of leveraging relevant information across related tasks as inductive biases to enhance learning performance has attracted significant interest. In contrast, attempts to emulate the human brains ability to generalize in optimization particularly in population-based evolutionary algorithms have received little attention to date. Recently, a novel evolutionary search paradigm, Evolutionary Multi-Task (EMT) optimization, has been proposed in the realm of evolutionary computation. In contrast to traditional evolutionary searches, which solve a single task in a single run, evolutionary multi-tasking algorithm conducts searches concurrently on multiple search spaces corresponding to different tasks or optimization problems, each possessing a unique function landscape. By exploiting the latent synergies among distinct problems, the superior search performance of EMT optimization in terms of solution quality and convergence speed has been demonstrated in a variety of continuous, discrete, and hybrid (mixture of continuous and discrete) tasks. This book discusses the foundations and methodologies of developing evolutionary multi-tasking algorithms for complex optimization, including in domains characterized by factors such as multiple objectives of interest, high-dimensional search spaces and NP-hardness.

• 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)

• Chapter 1. Design and Construction of a Dual Axis Solar Tracking System by Astronomical Algorithm.-
• Chapter 2. Estimation of Magnetic Flux linkage in SRM using various defuzzification techniques.-
• Chapter 3. Multilevel Inverter based STATCOM for Distribution System.-
• Chapter 4. Sensitivity Analysis and Design Optimization of Synchronous Reluctance and Permanent Magnet Motors.-
• Chapter 5. A New Heuristic algorithm for Economic Load Dispatch incorporating wind power.-
• Chapter 6. Enhanced Grasshopper Optimization Algorithm For Numerical Optimization.-
• Chapter 7. Eco-Routing - To Reduce Vehicle CO2 Emissions by CACC: An IoT Application.-
• Chapter 8. Fuzzy Sliding Mode Control of DC-DC Boost Converter with Right-Half Plane Zero.-
• Chapter 9. Liquid Level Control of Non Linear Process Using Big Bang - Big Crunch Optimization Based Controller.-
• Chapter 10. Impact of PV Cells and MPPT Controller on Power System Dynamics.-
• Chapter 11. Wavelet Feature Based Microcalcification Detection in Mammogram.-
• Chapter 12. Reliable Radiation Hardened Memory Cells for Single-Event Multiple Effects.-
• Chapter 13. Finger Vein Identification Using Deep Convolutional Generative Adversarial Networks.-
• Chapter 14. Computer Aided Detection of Malignant Mass in Mammogram using U-Net Architecture.-
• Chapter 15. Visualization and Evaluation of Methane Gas Leakage by Thermal Image Processing using Supervised Deep Learning Models.
• (source: Nielsen Book Data)

This book gathers selected papers presented at the 6th International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems, held at the Anna University, Chennai, India, from 20 to 22 April 2020. It covers advances and recent developments in various computational intelligence techniques, with an emphasis on the design of communication systems. In addition, it shares valuable insights into advanced computational methodologies such as neural networks, fuzzy systems, evolutionary algorithms, hybrid intelligent systems, uncertain reasoning techniques, and other machine learning methods and their application to decision-making and problem-solving in mobile and wireless communication networks.
(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)

• Networks
• Dynamical criticality in growing networks
• Effective Resistance based Weight Thresholding for Community Detection
• An Oracle for the Optimization of Underconstrained Compositions of Neural Networks - The Tick Hazard Use Case
• Droplets, Fluids, and Synthetic Biology
• Obstacles on the Pathway towards Chemical Programmability using Agglomerations of Droplets
• The Good, the Bad and the Ugly: droplet recognition by a "shootout"-heuristics
• Exploring the three-dimensional arrangement of droplets
• Geometric restrictions to the agglomeration of spherical particles
• Effectiveness of Dynamic Load Balancing in Parallel Execution of a Subsurface Flow Cellular Automata Model
• Two possible AI-related paths for bottom-up synthetic cell research
• Robot Systems
• A hybrid control system architecture for a mobile robot to provide an energy-efficient and fast data processing
• On the evolution of mechanisms for collective decision making in a swarm of robots
• A novel online adaptation mechanism in artificial systems provides phenotypic plasticity
• Computer Vision and Computational Creativity
• Exploration of Genetic Algorithms and CNN for Melanoma Classification
• Using Genetic Algorithms to optimize a deep learning based system for the prediction of cognitive impairments
• Autonomous inspections of power towers with an UAV
• Self-organizing Maps of Artificial Neural Classifiers - a Brain like Pin Factory
• Evolutionary Music: Statistical Learning and Novelty for automatic improvisation
• Semantic Search
• ARISE: Artificial Intelligence Semantic Search Engine
• Artificial Medicine and Pharmacy
• Influence of the Antigen Pattern Vector on the Dynamics in a Perceptron-based Artificial Immune - Tumour-Ecosystem during and after Radiation Therapy
• Two-level detection of dynamic organization in cancer evolution models
• Artificial chemical neural network for drug discovery applications
• Trade and Finance
• Information Flow Simulations in Multi-Dimensional and Dynamic Systems
• Investigation of the Ramsey-Pierce-Bowman Model
• Ethics in Computational Modelling
• Ethical Aspects of Computational Modelling in Science, Decision Support and Communication.

This book constitutes the proceedings of the 15th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2021, held in Winterthur, Switzerland, in September 2022. The 14 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 25 submissions. The papers are organized in the following topical sections: Networks; Droplets, Fluids, and Synthetic Biology; Robot Systems; Computer Vision and Computational Creativity; Semantic Search; Artificial Medicine and Pharmacy; Trade and Finance; Ethics in Computational Modelling.

• Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling: An Introduction.- Evolutionary Approaches for Project Portfolio Optimization: An Overview.- An Introduction to Evolutionary and Memetic Algorithms for Parameter Optimization.- An Overall Characterization of the Project Portfolio Optimization Problem and an Approach Based on Evolutionary Algorithms to Address It.- A New Model for the Project Portfolio Selection and Scheduling Problem with Defence Capability Options.- Analysis of New Approaches used in Portfolio Optimization: A Systematic Literature Review.- A Temporal Knapsack Approach to Defence Portfolio Selection.- A Decision Support System for Planning Portfolios of Supply Chain Improvement Projects in the Semiconductor Industry.
• (source: Nielsen Book Data)

This book consists of eight chapters, authored by distinguished researchers and practitioners, that highlight the state of the art and recent trends in addressing the project portfolio selection and scheduling problem (PPSSP) across a variety of domains, particularly defense, social programs, supply chains, and finance. Many organizations face the challenge of selecting and scheduling a subset of available projects subject to various resource and operational constraints. In the simplest scenario, the primary objective for an organization is to maximize the value added through funding and implementing a portfolio of projects, subject to the available budget. However, there are other major difficulties that are often associated with this problem such as qualitative project benefits, multiple conflicting objectives, complex project interdependencies, workforce and manufacturing constraints, and deep uncertainty regarding project costs, benefits, and completion times. It is well known that the PPSSP is an NP-hard problem and, thus, there is no known polynomial-time algorithm for this problem. Despite the complexity associated with solving the PPSSP, many traditional approaches to this problem make use of exact solvers. While exact solvers provide definitive optimal solutions, they quickly become prohibitively expensive in terms of computation time when the problem size is increased. In contrast, evolutionary and memetic computing afford the capability for autonomous heuristic approaches and expert knowledge to be combined and thereby provide an efficient means for high-quality approximation solutions to be attained. As such, these approaches can provide near real-time decision support information for portfolio design that can be used to augment and improve existing human-centric strategic decision-making processes. This edited book provides the reader with a broad overview of the PPSSP, its associated challenges, and approaches to addressing the problem using evolutionary and memetic computing.
(source: Nielsen Book Data)

• 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)

• SECTION A: Principles, Types, Theories, and Applications of Evolutionary Computation.
• 1. Evolutionary Computation for Optimized Solutions Towards Society 5
• .0.
• 2. Exploiting Machine Learning for Evolutionary Training of Machines.
• 3. Image Processing for Knowledge Management and Effective Information Extraction Through Optimization.
• 4. Evolutionary Computation Framework for Handling Resource Constraint Mobile Environments. SECTION B: Evolutionary Computation: Emerging Trends and Implications.
• 5. Smart Systems for Global Sustainability with Enhanced Computing.
• 6. Recommender System: Techniques for Optimized Decision Making.
• 7. Innovations in Healthcare Using Smart Systems Equipped with Evolutionary Computation.
• 8. Exploiting Evolutionary Computation Techniques for Service Industries. SECTION C: Next Generation Evolutionary Computation Techniques and Innovations.
• 9. Performance Analysis of Industrial Unit Using Evolutionary Computation Methods.
• 10. Reliability and Availability System Modeling with Evolutionary Computation Techniques.
• 11. Maintainability of System Through Evolutionary Computation Techniques.
• 12. Simulation and Analysis of Quadratic Assignment Problems Using Evolutionary. Computation. SECTION D: Evolutionary Computation for Society and Business Organizations.
• 13. Evolutionary Computation for Total Quality Management.
• 14. Consumer Behavior and Satisfaction Through Evolutionary Computation.
• 15. Decision Support System and Marketing Using Evolutionary Computation Techniques.
• 16. Performance Modeling and Optimization for the Stock Preparation with Evolutionary Computation.

"This book focuses on cutting-edge innovations and core theories, principles, and algorithms applicable to a wide area. Real-life applications, case studies, and examples are included along with emerging trends, design, and optimized solutions pivoting around the needs of Society 5.0. Evolutionary Computation with Intelligent Systems: A Multidisciplinary Approach to Society 5.0 provides a holistic view of Evolutionary Computation techniques with principles, procedures, and future applications with real-life examples. The book comprehensively explains Evolutionary Computation, design, principles, development trends, and optimization and describes how it can transform the operating context of the organization. It exemplifies the potential of Evolutionary Computation for the next generation and the role of cloud computing in shaping Society 5.0. It also provides insight into various platforms, paradigms, techniques, and tools used in diverse fields. This book appeals to all kinds of readers such as academicians, researchers, research scholars, and postgraduates"-- Provided by publisher.

• Improved Grey wolf Optimization based Feature selection and classification using CNN for Diabetic Retinopathy detection.- Feature Selection Using Modified Sine Cosine Algorithm with COVID-19 Dataset.- Blood Cell Image Denoising based on Tunicate Rat Swarm Optimization with Median Filter.- A Hybrid Approach for Deep Noise Suppression using Deep Neural Networks.- Human Health Care Systems Analysis for Cloud Data Structure of Biometric System using ECG Analysis.- Data mining for Solving Medical Diagnostics Problems.- Classification of Diabetic Retinopathy using Ensemble of Machine Learning Classifiers with IDRID Dataset.- Epileptic Seizure Prediction Using Geometrical Features Extracted From HRV Signal.- An Extensive Survey on Outlier Prediction using Mining and Learning Approaches.- Performance Comparison of Data Security Strategies in Fog Computing.- Design and Simulation of a Direct-PSK Based Telecommand Receiver for Small Satellite
• Analysis of Data Aggregation and Clustering Protocol in Wireless Sensor Networks using Machine Learning.- DetecSec : A Framework to Detect and Mitigate ARP Cache Poisoning Attacks.- PAPR Reduction in SDR based OFDM System.
• (source: Nielsen Book Data)

This book mainly reflects the recent research works in evolutionary computation technologies and mobile sustainable networks with a specific focus on computational intelligence and communication technologies that widely ranges from theoretical foundations to practical applications in enhancing the sustainability of mobile networks. Today, network sustainability has become a significant research domain in both academia and industries present across the globe. Also, the network sustainability paradigm has generated a solution for existing optimization challenges in mobile communication networks. Recently, the research advances in evolutionary computing technologies including swarm intelligence algorithms and other evolutionary algorithm paradigms are considered as the widely accepted descriptors for mobile sustainable networks virtualization, optimization, and automation. To deal with the emerging impacts on mobile communication networks, this book discusses about the state-of-the research works on developing a sustainable design and their implementation in mobile networks. With the advent of evolutionary computation algorithms, this book contributes varied research chapters to develop a new perspective on mobile sustainable networks.
(source: Nielsen Book Data)

• Swarm Intelligence and Its Applications.- Operational Technologies and Networked Multimedia Applications.- Wearable Computing and Intelligent Data Hiding.- Image Processing and Intelligent Applications.- Intelligent Multimedia Tools and Applications.- Technologies for Next-Generation Network Environments.- Recent Progress in Computational Electromagnetic Dynamics.- Future Cyber Security, Privacy and Forensics for Advanced systems.- Data Mining Techniques and its Applications.- Optimization Models in Deep Learning/Machine Learning.
• (source: Nielsen Book Data)

This book contains selected papers presented at ICGEC 2021, the 14th International Conference on Genetic and Evolutionary Computing, held from October 21-23, 2021 in Jilin City, China. The conference was technically co-sponsored by Springer, Northeast Electric Power University Fujian University of Technology, Shandong University of Science and Technology, and Western Norway University of Applied Sciences. It is intended as an international forum for the researchers and professionals in all areas of genetic and evolutionary computing. And the readers may learn the up-to-date techniques of the mentioned topics, including swarm intelligence, artificial intelligence, information hiding and data mining techniques, which can help them to bring new ideas or apply the designed approaches from the collected papers to their professional jobs.
(source: Nielsen Book Data)

• Automated Flower Species Identification by using Deep convolution neural network
• Information Retrieval for Cloud Forensics
• Machine Translation System Combination with Enhanced Alignments using Word Embeddings
• Geometry Based Machining Feature Retrieval with Inductive Transfer Learning
• Grapheme to phoneme conversion for Malayalam Speech Using encoder-decoder Architecture
• Usage of Block Chain Technology in e-Voting System using Private Block Chain
• Bengali Visual Genome: A Multimodal Dataset for Machine Translation and Image Captioning
• Deep Learning based Mosquito Species detection using Wingbeats frequencies
• Developments in Capsule Network Architecture: A Review
• Computer aided segmentation of polyps using Mask R-CNN and approach to reduce false positives
• Image GPT with Super Resolution.

This book presents the proceedings of the 9th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2021), held at NIT Mizoram, Aizwal, Mizoram, India, during June 25-26, 2021. FICTA conference aims to bring together researchers, scientists, engineers, and practitioners to exchange their new ideas and experiences in the domain of intelligent computing theories with prospective applications to various engineering disciplines. This volume covers broad areas of Intelligent Data Engineering and Analytics. The conference papers included herein presents both theoretical as well as practical aspects of data intensive computing, data mining, big data, knowledge management, intelligent data acquisition and processing from sensors, data communication networks protocols and architectures, etc. The volume will also serve as a knowledge centre for students of post-graduate level in various engineering disciplines. .

• Amended draft Table of Contents
• 1.
• Introduction and relevance
• a.
• Who this book benefits
• b.
• Required background
• c.
• Review of population genetic/genomic simulation resources
• d.
• When to write your own simulations
• 2.
• Retrospective and prospective simulation
• a.
• Retrospective, coalescent simulation
• b.
• Prospective simulation
• c.
• Individual-based models
• 3.
• Data structures and computational efficiency
• a.
• Data structures
• b.
• Computer clusters
• c.
• Graphical processor units (GPU) programming
• Part I. Simulating the five factors that affect population dynamics and genetic diversity
• 4.
• Mutation
• a.
• Background and theory
• b.
• The bitset as a data structure for storing genetic sequence data
• c.
• Writing individual and population classes
• d.
• Common types of mutation
• i.
• Point mutations
• ii. Indels
• iii. Microsatellites
• iv. AFLPs
• e.
• Haplotypes
• 5.
• Population size and genetic drift
• a.
• Background and theory
• b.
• Fixation of alleles
• c.
• Demographic change: expansions and bottlenecks
• 6.
• Migration and population structure
• a.
• Background and theory
• b.
• Panmixia
• c.
• Isolation by barrier
• d.
• Isolation by distance
• e.
• Admixture
• f.
• Metapopulations
• 7.
• Meiotic recombination
• a.
• Background and theory
• b.
• Unlinked loci and independent assortment
• c.
• Linked loci and crossing-over
• d.
• Linked loci and gene conversion
• 8.
• Natural selection
• a.
• Background and theory
• b.
• Fitness
• c.
• Viability and fecundity selection
• d.
• Positive natural selection
• e.
• Purifying natural selection and background selection
• f.
• Frequency-dependent selection
• g.
• Assortative mating
• h.
• Selection on a protein-coding gene
• Part II. Adding biological and ecological realism
• 9.
• Implementing all five factors simultaneously
• a.
• A generation function: the order of things
• b.
• Birth and death
• c.
• Overlapping vs. non-overlapping generations
• d.
• Using coalescent simulation to obtain random starting populations
• 10. Modeling different life histories
• a.
• Ploidy
• b.
• Monoecious, dioecious, and hermaphroditic species
• 11. Spatially-explicit simulation
• a.
• Neutral evolution
• b.
• The impact of landscape on dispersal
• c.
• The impact of environment on fitness
• Part III. Statistical inference in population genetics
• 12. Calculating summary statistics and visualization
• a.
• Sequence-based summary statistics
• b.
• Locus-specific summary statistics
• c.
• Null distributions
• d.
• Visualizing simulated and empirical data
• 13. Approximate Bayesian computation: preliminaries
• a.
• Statistical and historical background
• b.
• Parameters
• c.
• Prior distributions
• d.
• Sufficient summary statistics
• e.
• Tolerance level
• f.
• The relevance of data type: SNPs, microsatellites, AFLPs
• 14. Approximate Bayesian computation: implementation
• a.
• Rejection algorithms
• b.
• Regression-based algorithms
• c.
• Markov Chain Monte Carlo algorithms
• d.
• Sequential Monte Carlo algorithms
• e.
• Hierarchical models
• f.
• Model selection
• g.
• Parameter estimation
• h.
• Marginal, joint, and conditional distributions
• i.
• Posterior predictive distributions and model validation
• j.
• When simulation is costly: Approximate approximate Bayesian computation
• Part IV. In-depth examples
• 15. Comparing simulated genetic data to 1000 Genomes data
• 16. The spread of the invasive species Japanese hops in the Upper Midwest, USA
• Appendices
• C++: Review and reference
• R: Review and reference.
• (source: Nielsen Book Data)

Dramatic advances in computing power enable simulation of DNA sequences generated by complex microevolutionary scenarios that include mutation, population structure, natural selection, meiotic recombination, demographic change, and explicit spatial geographies. Although retrospective, coalescent simulation is computationally efficient-and covered here-the primary focus of this book is forward-in-time simulation, which frees us to simulate a wider variety of realistic microevolutionary models. The book walks the reader through the development of a forward-in-time evolutionary simulator dubbed FORward Time simUlatioN Application (FORTUNA). The capacity of FORTUNA grows with each chapter through the addition of a new evolutionary factor to its code. Each chapter also reviews the relevant theory and links simulation results to key evolutionary insights. The book addresses visualization of results through development of R code and reference to more than 100 figures. All code discussed in the book is freely available, which the reader may use directly or modify to better suit his or her own research needs. Advanced undergraduate students, graduate students, and professional researchers will all benefit from this introduction to the increasingly important skill of population genetic simulation. .
(source: Nielsen Book Data)

• 1. Introduction and Scientific Goals
• 2. State of the Art: Grammatical Inference
• 3. State of the Art: Genetic Algorithms and Premature Convergence
• 4. Genetic Algorithms and Grammatical Inference
• 5. Performance Analysis of Genetic Algorithm for Grammatical Inference
• 6. Applications of Grammatical Inference Methods and Future Development.
• (source: Nielsen Book Data)

State of the Art on Grammatical Inference Using Evolutionary Method presents an approach for grammatical inference (GI) using evolutionary algorithms. Grammatical inference deals with the standard learning procedure to acquire grammars based on evidence about the language. It has been extensively studied due to its high importance in various fields of engineering and science. The book's prime purpose is to enhance the current state-of-the-art of grammatical inference methods and present new evolutionary algorithms-based approaches for context free grammar induction. The book's focus lies in the development of robust genetic algorithms for context free grammar induction. The new algorithms discussed in this book incorporate Boolean-based operators during offspring generation within the execution of the genetic algorithm. Hence, the user has no limitation on utilizing the evolutionary methods for grammatical inference.
(source: Nielsen Book Data)