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• 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. .
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• 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.
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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.
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• 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.
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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.
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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.
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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.
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• 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.
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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.
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• Preface Acknowledgments Introduction Contrasting Representations for Maze Generation Dual Mazes Terrain Maps Cellular Automata Based Maps Decomposition, Tiling, and Assembly Bibliography Author's Biography.
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Automatic content generation is the production of content for games, web pages, or other purposes by procedural means. Search-based automatic content generation employs search-based algorithms to accomplish automatic content generation. This book presents a number of different techniques for search-based automatic content generation where the search algorithm is an evolutionary algorithm. The chapters treat puzzle design, the creation of small maps or mazes, the use of L-systems and a generalization of L-system to create terrain maps, the use of cellular automata to create maps, and, finally, the decomposition of the design problem for large, complex maps culminating in the creation of a map for a fantasy game module with designersupplied content and tactical features. The evolutionary algorithms used for the different types of content are generic and similar, with the exception of the novel sparse initialization technique are presented in Chapter 2. The points where the content generation systems vary are in the design of their fitness functions and in the way the space of objects being searched is represented. A large variety of different fitness functions are designed and explained, and similarly radically different representations are applied to the design of digital objects all of which are, essentially, maps for use in games.
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• 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.
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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.
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• 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. .

• AI Techniques and applications.- Data Analytics and Technologies.- Data Mining and Image Processing.- Machine & Statistical Learning. .
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This book constitutes the refereed proceedings of the 6th International Conference on Soft Computing in Data Science, SCDS 2021, which was held virtually in November 2021. The 31 revised full papers presented were carefully reviewed and selected from 79 submissions. The papers are organized in topical sections on AI techniques and applications; data analytics and technologies; data mining and image processing; machine & statistical learning. .
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• List of Contributors vii Editors' Biographies xi Preface xv Acknowledgments xvii
• 1 Evolutionary Computation in Scheduling: A Scientometric Analysis 1 Amir H. Gandomi, Ali Emrouznejad, and Iman Rahimi
• 2 Role and Impacts of Ant Colony Optimization in Job Shop Scheduling Problems: A Detailed Analysis 11 P. Deepalakshmi and K. Shankar
• 3 Advanced Ant Colony Optimization in Healthcare Scheduling 37 Reza Behmanesh, Iman Rahimi, Mostafa Zandieh, and Amir H. Gandomi
• 4 Task Scheduling in Heterogeneous Computing Systems Using Swarm Intelligence 73 S. Sarathambekai and K. Umamaheswari
• 5 Computationally Efficient Scheduling Schemes for Multiple Antenna Systems Using Evolutionary Algorithms and Swarm Optimization 105 Prabina Pattanayak and Preetam Kumar
• 6 An Efficient Modified Red Deer Algorithm to Solve a Truck Scheduling Problem Considering Time Windows and Deadline for Trucks' Departure 137 Amir Mohammad Fathollahi-Fard, Abbas Ahmadi, and Mohsen S. Sajadieh
• 7 Application of Sub-Population Scheduling Algorithm in Multi-Population Evolutionary Dynamic Optimization 169 Javidan Kazemi Kordestani and Mohammad Reza Meybodi
• 8 Task Scheduling in Cloud Environments: A Survey of Population-Based Evolutionary Algorithms 213 Fahimeh Ramezani, Mohsen Naderpour, Javid Taheri, Jack Romanous, and Albert Y. Zomaya
• 9 Scheduling of Robotic Disassembly in Remanufacturing Using Bees Algorithms 257 Jiayi Liu, Wenjun Xu, Zude Zhou, and Duc Truong Pham
• 10 A Modified Fireworks Algorithm to Solve the Heat and Power Generation Scheduling Problem in Power System Studies 299 Mohammad Sadegh Javadi, Ali Esmaeel Nezhad, Seyed-Ehsan Razavi, Abdollah Ahmadi, and Joao P.S. Catalao Index 327.
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Presents current developments in the field of evolutionary scheduling and demonstrates the applicability of evolutionary computational techniques to solving scheduling problems This book provides insight into the use of evolutionary computations (EC) in real-world scheduling, showing readers how to choose a specific evolutionary computation and how to validate the results using metrics and statistics. It offers a spectrum of real-world optimization problems, including applications of EC in industry and service organizations such as healthcare scheduling, aircraft industry, school timetabling, manufacturing systems, and transportation scheduling in the supply chain. It also features problems with different degrees of complexity, practical requirements, user constraints, and MOEC solution approaches. Evolutionary Computation in Scheduling starts with a chapter on scientometric analysis to analyze scientific literature in evolutionary computation in scheduling. It then examines the role and impacts of ant colony optimization (ACO) in job shop scheduling problems, before presenting the application of the ACO algorithm in healthcare scheduling. Other chapters explore task scheduling in heterogeneous computing systems and truck scheduling using swarm intelligence, application of sub-population scheduling algorithm in multi-population evolutionary dynamic optimization, task scheduling in cloud environments, scheduling of robotic disassembly in remanufacturing using the bees algorithm, and more. This book: Provides a representative sampling of real-world problems currently being tackled by practitioners Examines a variety of single-, multi-, and many-objective problems that have been solved using evolutionary computations, including evolutionary algorithms and swarm intelligence Consists of four main parts: Introduction to Scheduling Problems, Computational Issues in Scheduling Problems, Evolutionary Computation, and Evolutionary Computations for Scheduling Problems Evolutionary Computation in Scheduling is ideal for engineers in industries, research scholars, advanced undergraduates and graduate students, and faculty teaching and conducting research in Operations Research and Industrial Engineering.
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• EvoSET: Hybrid Algorithms Based on Integer Programming for the Search of Prioritized Test Data in Software Product Lines.- On the Use of Smelly Examples to Detect Code Smells in JavaScript.- Deep Parameter Tuning of Concurrent Divide and Conquer Algorithms in Akka.- Focusing Learning-based Testing away from Known Weaknesses.- Polytypic Genetic Programming.- Evolving Rules for Action Selection in Automated Testing via Genetic Programming - A First Approach.- EvoSTOC: A New Multi-swarm Particle Swarm Optimization for Robust Optimization over Time.- The Static and Stochastic VRP with Time Windows and Both Random Customers and Reveal Times.- Pre-Scheduled Colony Size Variation in Dynamic Environments.- An Online Packing Heuristic for the Three-dimensional Container Loading Problem in Dynamic Environments and the Physical Internet.- Advancing Dynamic Evolutionary Optimization Using In-Memory Database Technology.- Road Traffic Rules Synthesis Using Grammatical Evolution.- Solving Dynamic Graph Coloring Problem Using Dynamic Pool Based Evolutionary Algorithm.- General: Meta-Heuristics for Improved RF Emitter Localization.- Automated Design of Genetic Programming Classification Algorithms Using a Genetic Algorithm.
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The two volumes LNCS 10199 and 10200 constitute the refereed conference proceedings of the 20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017, held in Amsterdam, The Netherlands, in April 2017, colocated with the Evo* 2016 events EuroGP, EvoCOP, and EvoMUSART. The 46 revised full papers presented together with 26 poster papers were carefully reviewed and selected from 108 submissions. EvoApplications 2016 consisted of the following 13 tracks: EvoBAFIN (natural computing methods in business analytics and finance), EvoBIO (evolutionary computation, machine learning and data mining in computational biology), EvoCOMNET (nature-inspired techniques for telecommunication networks and other parallel and distributed systems), EvoCOMPLEX (evolutionary algorithms and complex systems), EvoENERGY (evolutionary computation in energy applications), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in image analysis, signal processing, and pattern recognition), EvoINDUSTRY (nature-inspired techniques in industrial settings), EvoKNOW (knowledge incorporation in evolutionary computation), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoROBOT (evolutionary robotics), EvoSET (nature-inspired algorithms in software engineering and testing), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments). .
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• A Computational Study of Neighborhood Operators for Job-shop Scheduling Problems with Regular Objectives.- A Genetic Algorithm for Multi-Component Optimization Problems: the Case of the Travelling Thief Problem.- A Hybrid Feature Selection Algorithm Based on Large Neighborhood Search.- A Memetic Algorithm to Maximise the Employee Substitutability in Personnel Shift Scheduling.- Construct, Merge, Solve and Adapt versus Large Neighborhood Search for Solving the Multi-Dimensional Knapsack Problem: Which One Works Better When.- Decomposing SAT Instances with Pseudo Backbones.- Efficient Consideration of Soft Time Windows in a Large Neighborhood Search for the Districting and Routing Problem for Security Control.- Estimation of Distribution Algorithms for the Firefighter Problem.- LCS-Based Selective Route Exchange Crossover for the Pickup and Delivery Problem with Time Windows.- Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO.- Optimizing Charging Station Locations for Electric Car-Sharing Systems.- Selection of Auxiliary Objectives Using Landscape Features and Offline Learned Classifier.- Sparse, Continuous Policy Representations for Uniform Online Bin Packing via Regression of Interpolants.- The Weighted Independent Domination Problem: ILP Model and Algorithmic .
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This book constitutes the refereed proceedings of the 17th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2017, held in Amsterdam, The Netherlands, in April 2017, co-located with the Evo*2017 events EuroGP, EvoMUSART and EvoApplications. The 16 revised full papers presented were carefully reviewed and selected from 39 submissions. The papers cover both empirical and theoretical studies on a wide range of academic and real-world applications. The methods include evolutionary and memetic algorithms, large neighborhood search, estimation of distribution algorithms, beam search, ant colony optimization, hyper-heuristics and matheuristics. Applications include both traditional domains, such as knapsack problem, vehicle routing, scheduling problems and SAT; and newer domains such as the traveling thief problem, location planning for car-sharing systems and spacecraft trajectory optimization. Papers also study important concepts such as pseudo-backbones, phase transitions in local optima networks, and the analysis of operators. This wide range of topics makes the EvoCOP proceedings an important source for current research trends in combinatorial optimization.
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• 1 Evolutionary Computation in Economics and Finance: An Overview of the Book.- 2 Playing Games with Genetic Algorithms.- 3 Genetic Algorithm Learning and Economic Evolution.- 4 Using Symbolic Regression to Infer Strategies from Experimental Data.- 5 The Efficiency of an Artificial Double Auction Stock Market with Neural Learning Agents.- 6 On AIE-ASM: Software to Simulate Artificial Stock Markets with Genetic Programming.- 7 Exchange Rate Volatility in the Artificial Foreign Exchange Market.- 8 Using an Artificial Market Approach to Analyze Exchange Rate Scenarios.- 9 Emulating Trade in Emissions Permits: An Application of Genetic Algorithms.- 10 Cooperative Computation with Market Mechanism.- 11 Hysteresis in an Evolutionary Labor Market with Adaptive Search.- 12 Computable Learning, Neural Networks and Institutions.- 13 On Two Types of GA-Learning.- 14 Evolutionary Computation and Economic Models: Sensitivity and Unintended Consequences.- 15 Tinkering with Genetic Algorithms: Forecasting and Data Mining in Finance and Economics.- 16 Forecasting Ability But No Profitability: An Empirical Evaluation of Genetic Algorithm-Optimised Technical Trading Rules.- 17 Evolutionary Induction of Trading Models.- 18 Optimizing Technical Trading Strategies with Split Search Genetic Algorithms.- 19 GP Forecasts of Stock Prices for Profitable Trading.- 20 Option Pricing Via Genetic Programming.- 21 Evolutionary Computation in Option Pricing: Determining Implied Volatilities Based on American Put Options.- 22 Evolutionary Computation in Economics and Finance: A Bibliography.
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After a decade's development, evolutionary computation (EC) proves to be a powerful tool kit for economic analysis. While the demand for this equipment is increasing, there is no volume exclusively written for economists. This volume for the first time helps economists to get a quick grasp on how EC may support their research. A comprehensive coverage of the subject is given, that includes the following three areas: game theory, agent-based economic modelling and financial engineering. Twenty leading scholars from each of these areas contribute a chapter to the volume. The reader will find himself treading the path of the history of this research area, from the fledgling stage to the burgeoning era. The results on games, labour markets, pollution control, institution and productivity, financial markets, trading systems design and derivative pricing, are new and interesting for different target groups. The book also includes informations on web sites, conferences, and computer software.
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• Introduction to Optimization.- Classical Optimization Algorithms.- Evolutionary and Swarm Optimization.- Introduction to Machine Learning.- Data-Driven Surrogate-Assisted Evolutionary Optimization.- Multi-Surrogate-Assisted Single-Objective Optimization.- Surrogate-Assisted Multi-Objective Evolutionary Optimization.
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Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.
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• Introduction
• Preliminary
• Optinformatics Within a Single Problem Domain
• Optinformatics Across Heterogeneous Problem Domains and Solvers
• Potential Research Directions.

This book provides readers the recent algorithmic advances towards realizing the notion of optinformatics in evolutionary learning and optimization. The book also provides readers a variety of practical applications, including inter-domain learning in vehicle route planning, data-driven techniques for feature engineering in automated machine learning, as well as evolutionary transfer reinforcement learning. Through reading this book, the readers will understand the concept of optinformatics, recent research progresses in this direction, as well as particular algorithm designs and application of optinformatics. Evolutionary algorithms (EAs) are adaptive search approaches that take inspiration from the principles of natural selection and genetics. Due to their efficacy of global search and ease of usage, EAs have been widely deployed to address complex optimization problems occurring in a plethora of real-world domains, including image processing, automation of machine learning, neural architecture search, urban logistics planning, etc. Despite the success enjoyed by EAs, it is worth noting that most existing EA optimizers conduct the evolutionary search process from scratch, ignoring the data that may have been accumulated from different problems solved in the past. However, today, it is well established that real-world problems seldom exist in isolation, such that harnessing the available data from related problems could yield useful information for more efficient problem-solving. Therefore, in recent years, there is an increasing research trend in conducting knowledge learning and data processing along the course of an optimization process, with the goal of achieving accelerated search in conjunction with better solution quality. To this end, the term optinformatics has been coined in the literature as the incorporation of information processing and data mining (i.e., informatics) techniques into the optimization process. The primary market of this book is researchers from both academia and industry, who are working on computational intelligence methods and their applications. This book is also written to be used as a textbook for a postgraduate course in computational intelligence emphasizing methodologies at the intersection of optimization and machine learning. .
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• On Restricting Real-Valued Genotypes in Evolutionary Algorithms.- Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions.- Co-Optimising Robot Morphology and Controller in a Simulated Open-ended Environment.- Multi-objective workforce allocation in construction projects.- Generating Duplex Routes for Robust Bus Transport Network by Improved Multi-objective Evolutionary Algorithm based on Decomposition.- Combining Multi-objective Evolutionary Algorithms with deep generative models towards focused molecular design.- A Multi-Objective Evolutionary Algorithm Approach for Optimizing Part Quality Aware Assembly Job Shop Scheduling Problems.- Evolutionary Grain-Mixing to Improve Profitability in Farming Winter Wheat.- Automatic Modular Design of Behavior Trees for Robot Swarms with Communication Capabilites.- Salp Swarm Optimization Search Based Feature Selection for Enhanced Phishing Websites Detection.- Real Time Optimisation of Traffic Signals to Prioritise Public Transport.- Adaptive Covariance Pattern Search.- Evaluating the Success-History based Adaptive Differential Evolution in the Protein Structure Prediction problem.- Beyond Body Shape and Brain: Evolving the Sensory Apparatus of Voxel-based Soft Robots.- Desirable Objective Ranges in Preference-based Evolutionary Multiobjective Optimization.- Improving Search Efficiency and Diversity of Solutions in Multiobjective Binary Optimization by Using Metaheuristics plus Integer Linear Programming.- Automated, Explainable Rule Extraction from MAP-Elites archives.- EDM-DRL: Toward Stable Reinforcement Learning through Ensembled Directed Mutation.- Continuous Ant-Based Neural Topology.- Playing with Dynamic Systems - Battling Swarms in Virtual Reality.- EvoCraft: A New Challenge for Open-Endedness.- A Profile-Based 'GrEvolutionary' Hearthstone Agent.- Modelling Asthma Patients' Responsiveness to Treatment Using Feature Selection and Evolutionary Computation.- Bayesian Networks for Mood Prediction Using Unobtrusive Ecological Momentary Assessments.- A Multi-Objective Multi-Type Facility Location Problem for the Delivery of Personalised Medicine.- RDE-OP: A Region-Based Differential Evolution Algorithm Incorporation Opposition-Based Learning for Optimising the Learning Process of Multi-Layer Neural Networks.- Estimation of Grain-level Residual Stresses in a Quenched Cylindrical Sample of Aluminum Alloy AA5083 using Genetic Programming.- EDA-based optimization of blow-off valve positions for centrifugal compressor systems.- 3D-2D Registration using X-ray Simulation and CMA-ES.- Lateralized Approach for Robustness AgainstAttacks in Emotion Categorization from Images.- Improved Crowding Distance in Multi-objective Optimization for Feature Selection in Classification.- Deep Optimisation: Multi-Scale Evolution by Inducing and Searching in Deep Representations.- Evolutionary Planning in Latent Space.- Utilizing the Untapped Potential of Indirect Encoding for Neural Networks with Meta Learning.- Effective Universal Unrestricted Adversarial Attacks using a MOE Approach.- Improving Distributed Neuroevolution Using Island Extinction and Repopulation.- An Experimental Study of Weight Initialization and Lamarckian Inheritance on Neuroevolution.- Towards Feature-Based Performance Regression Using Trajectory Data.- Demonstrating the Evolution of GANs through t-SNE.- Optimising diversity in classifier ensembles of classification trees.- WILDA: Wide Learning of Diverse Architectures for Classification of Large Datasets.- Evolving Character-Level DenseNet Architectures using Genetic Programming.- Transfer Learning for Automated Test Case Prioritization using XCSF.- On the Effects of Absumption for XCS with Continuous-Valued Inputs.- A NEAT Visualisation of Neuroevolution Trajectories.- Evaluating Models with Dynamic Sampling Holdout.- Event-driven multi-algorithm optimization: mixing Swarm and Evolutionary strategies.- TensorGP - Genetic Programming Engine in TensorFlow.- A novel evolutionary approach for IoT-based water contaminant detection.- Evolutionary Algorithms for Roughness Coefficient Estimation in River Flow Analyses.- EA-based ASV Trajectory Planner for Pollution Detection in Lentic Waters.
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This book constitutes the refereed proceedings of the 24th International Conference on Applications of Evolutionary Computation, EvoApplications 2021, held as part of Evo*2021, as Virtual Event, in April 2021, co-located with the Evo*2021 events EuroGP, EvoCOP, and EvoMUSART. The 51 revised full papers presented in this book were carefully reviewed and selected from 78 submissions. The papers cover a wide spectrum of topics, ranging from applications of evolutionary computation; applications of deep bioinspired algorithms; soft computing applied to games; machine learning and AI in digital healthcare and personalized medicine; evolutionary computation in image analysis, signal processing and pattern recognition; evolutionary machine learning; parallel and distributed systems; and applications of nature inspired computing for sustainability and development. .
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• Faster Result Retrieval from Health Care Product Sales Data Warehouse using Materialized Queries.- Feature Selection using Ant Colony Optimization and Weighted Visibility Graph.- From Generic to Custom: A Survey on Role of Machine Learning in Pharmacogenomics, Its Applications and Challenges.- Personalized Structure Balance Theory Based Movie Recommendation System.- Machine Learning Techniques for the Investigation of Phishing Websites.
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This book presents the proceedings of 8th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2020), which aims to bring together researchers, scientists, engineers and practitioners to share new ideas and experiences in the domain of intelligent computing theories with prospective applications to various engineering disciplines. The book is divided into two volumes: Evolution in Computational Intelligence (Volume 1) and Intelligent Data Engineering and Analytics (Volume 2). Covering a broad range of topics in computational intelligence, the book features papers on theoretical as well as practical aspects of areas such as ANN and genetic algorithms, computer interaction, intelligent control optimization, evolutionary computing, intelligent e-learning systems, machine learning, mobile computing, and multi-agent systems. As such, it is a valuable reference resource for postgraduate students in various engineering disciplines.
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• A Novel Ant Colony Optimization Strategy for the Quantum Circuit Compilation Problem.- Hybridization of Racing Methods with Evolutionary Operators for Simulation Optimization of Traffic Lights Programs.- Decomposition-based Multi-objective Landscape Features and Automated Algorithm Selection.- MATE: A Model-Based Algorithm Tuning Engine.- An Improvement Heuristic Based on Variable Neighborhood Search for a Dynamic Orienteering Problem.- Runtime Analysis of the (mu+1)-EA on the Dynamic BinVal Function.- Tabu-Driven Quantum Neighborhood Samplers.- On Hybrid Heuristics for Steiner Trees on the Plane with Obstacles.- Flowshop NEH-Based Heuristic Recommendation.- Stagnation Detection with Randomized Local Search.- An Artificial Immune System for Black Box Test Case Selection.- Symmetry Breaking for Voting Mechanisms.- A Heuristic Algorithm for School Bus Routing with Bus Stop Selection.- Hybrid Heuristic and Metaheuristic for Solving Electric Vehicle Charging Scheduling Problem.
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This book constitutes the refereed proceedings of the 21st European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2021, held as part of Evo*2021, as Virtual Event, in April 2021, co-located with the Evo*2021 events: EvoMUSART, EvoApplications, and EuroGP. The 14 revised full papers presented in this book were carefully reviewed and selected from 42 submissions. They cover a wide spectrum of topics, ranging from the foundations of evolutionary algorithms and other search heuristics to their accurate design and application to combinatorial optimization problems. Fundamental and methodological aspects deal with runtime analysis, the structural properties of fitness landscapes, the study of core components of metaheuristics, the clever design of their search principles, and their careful selection and configuration. Applications cover problem domains such as scheduling, routing, search-based software engineering and general graph problems. The range of topics covered in this volume reflects the current state of research in the fields of evolutionary computation and combinatorial optimization.
(source: Nielsen Book Data)

• Optimal Resource Sharing amongst Device-to-Device Communication using Particle Swarm Algorithm.- Real Time Human Locator and Advance Home Security Appliances.- A Novel Implementation of Haptic Robotic Arm.- Design of Book Recommendation System Using Sentiment Analysis.- Review of Python for Solar Photovoltaic Systems.- Key Stroke Dynamics for User Verification.- The Role of Predictive Data Analytics in Retailing.- Improved Scheduling Algorithm for Load Balancing in Cloud Computing.- Generating Automobile Images Dynamically from Text Description.- Generating Automobile Images Dynamically from Text Description.- Identification of Online Auction Bidding Robots using Machine Learning.- Analysis of Attention Deficit Hyperactivity Disorder using Various Classifiers.- Generating Feasible Path Between Path Testing and Data Flow Testing.- Parking Management System Using Internet of Things.- Binomial Logistic Regression Resource Optimized Routing In MANET.- Homo Sapiens Sapiens Diabetes Mellitus Detection and Classification.- Improved Logistic Map based Algorithm for Biometric Image Encryption.- Accuracy Analysis of Similarity Measures in Surprise Framework.- Tunable Access Control for Data Sharing in Cloud.- Conceptual Online Education using E-Learning Platform of Cloud Computing.- Efficient Deployment of Web Application in Serverless Environment.
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This book features selected research papers presented at the International Conference on Evolutionary Computing and Mobile Sustainable Networks (ICECMSN 2020), held at the Sir M. Visvesvaraya Institute of Technology on 20-21 February 2020. Discussing advances in evolutionary computing technologies, including swarm intelligence algorithms and other evolutionary algorithm paradigms which are emerging as widely accepted descriptors for mobile sustainable networks virtualization, optimization and automation, this book is a valuable resource for researchers in the field of evolutionary computing and mobile sustainable networks.
(source: Nielsen Book Data)

• Introduction to Evolutionary Data Clustering and its Applications.- A Comprehensive Review of Evaluation and Fitness Measures for Evolutionary Data Clustering.- A Grey Wolf based Clustering Algorithm for Medical Diagnosis Problems.- EEG-based Person Identification Using Multi-Verse Optimizer As Unsupervised Clustering Techniques.- Review of Evolutionary Data Clustering Algorithms for Image Segmentation.- Classification Approach based on Evolutionary Clustering and its Application for Ransomware Detection.
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This book provides an in-depth analysis of the current evolutionary clustering techniques. It discusses the most highly regarded methods for data clustering. The book provides literature reviews about single objective and multi-objective evolutionary clustering algorithms. In addition, the book provides a comprehensive review of the fitness functions and evaluation measures that are used in most of evolutionary clustering algorithms. Furthermore, it provides a conceptual analysis including definition, validation and quality measures, applications, and implementations for data clustering using classical and modern nature-inspired techniques. It features a range of proven and recent nature-inspired algorithms used to data clustering, including particle swarm optimization, ant colony optimization, grey wolf optimizer, salp swarm algorithm, multi-verse optimizer, Harris hawks optimization, beta-hill climbing optimization. The book also covers applications of evolutionary data clustering in diverse fields such as image segmentation, medical applications, and pavement infrastructure asset management.
(source: Nielsen Book Data)