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• EvoCOMNET Contributions.- Web Application Security through Gene Expression Programming.- Location Discovery in Wireless Sensor Networks Using a Two-Stage Simulated Annealing.- Wireless Communications for Distributed Navigation in Robot Swarms.- An Evolutionary Algorithm for Survivable Virtual Topology Mapping in Optical WDM Networks.- Extremal Optimization as a Viable Means for Mapping in Grids.- Swarm Intelligence Inspired Multicast Routing: An Ant Colony Optimization Approach.- A Framework for Evolutionary Peer-to-Peer Overlay Schemes.- Multiuser Scheduling in HSDPA with Particle Swarm Optimization.- Efficient Signal Processing and Anomaly Detection in Wireless Sensor Networks.- Peer-to-Peer Optimization in Large Unreliable Networks with Branch-and-Bound and Particle Swarms.- Evolving High-Speed, Easy-to-Understand Network Intrusion Detection Rules with Genetic Programming.- Soft Computing Techniques for Internet Backbone Traffic Anomaly Detection.- Testing Detector Parameterization Using Evolutionary Exploit Generation.- Ant Routing with Distributed Geographical Localization of Knowledge in Ad-Hoc Networks.- Discrete Particle Swarm Optimization for Multiple Destination Routing Problems.- EvoENVIRONMENT Contributions.- Combining Back-Propagation and Genetic Algorithms to Train Neural Networks for Ambient Temperature Modeling in Italy.- Estimating the Concentration of Nitrates in Water Samples Using PSO and VNS Approaches.- Optimal Irrigation Scheduling with Evolutionary Algorithms.- Adaptive Land-Use Management in Dynamic Ecological System.- EvoFIN Contributions.- Evolutionary Money Management.- Prediction of Interday Stock Prices Using Developmental and Linear Genetic Programming.- An Introduction to Natural Computing in Finance.- Evolutionary Approaches for Estimating a Coupled Markov Chain Model for Credit Portfolio Risk Management.- Knowledge Patterns in Evolutionary Decision Support Systems for Financial Time Series Analysis.- Predicting Turning Points in Financial Markets with Fuzzy-Evolutionary and Neuro-Evolutionary Modeling.- Comparison of Multi-agent Co-operative Co-evolutionary and Evolutionary Algorithms for Multi-objective Portfolio Optimization.- Dynamic High Frequency Trading: A Neuro-Evolutionary Approach.- EvoGAMES Contributions.- Decay of Invincible Clusters of Cooperators in the Evolutionary Prisoner's Dilemma Game.- Evolutionary Equilibria Detection in Non-cooperative Games.- Coevolution of Competing Agent Species in a Game-Like Environment.- Simulation Minus One Makes a Game.- Evolving Simple Art-Based Games.- Swarming for Games: Immersion in Complex Systems.- Fitness Diversity Parallel Evolution Algorithms in the Turtle Race Game.- Evolving Strategies for Non-player Characters in Unsteady Environments.- Grid Coevolution for Adaptive Simulations: Application to the Building of Opening Books in the Game of Go.- Evolving Teams of Cooperating Agents for Real-Time Strategy Game.- EvoHOT Contributions.- Design Optimization of Radio Frequency Discrete Tuning Varactors.- An Evolutionary Path Planner for Multiple Robot Arms.- Evolutionary Optimization of Number of Gates in PLA Circuits Implemented in VLSI Circuits.- Particle Swarm Optimisation as a Hardware-Oriented Meta-heuristic for Image Analysis.- EvoIASP Contributions.- A Novel GP Approach to Synthesize Vegetation Indices for Soil Erosion Assessment.- Flies Open a Door to SLAM.- Genetic Image Network for Image Classification.- Multiple Network CGP for the Classification of Mammograms.- Evolving Local Descriptor Operators through Genetic Programming.- Evolutionary Optimization for Plasmon-Assisted Lithography.- An Improved Multi-objective Technique for Fuzzy Clustering with Application to IRS Image Segmentation.- EvoINTERACTION Contributions.- Interactive Evolutionary Evaluation through Spatial Partitioning of Fitness Zones.- Fractal Evolver: Interactive Evolutionary Design of Fractals with Grid Computing.- Humorized Computational Intelligence towards User-Adapted Systems with a Sense of Humor.- Innovative Chance Discovery - Extracting Customers' Innovative Concept.- EvoMUSART Contributions.- Evolving Approximate Image Filters.- On the Role of Temporary Storage in Interactive Evolution.- Habitat: Engineering in a Simulated Audible Ecosystem.- The Evolution of Evolutionary Software: Intelligent Rhythm Generation in Kinetic Engine.- Filterscape: Energy Recycling in a Creative Ecosystem.- Evolved Ricochet Compositions.- Life's What You Make: Niche Construction and Evolutionary Art.- Global Expectation-Violation as Fitness Function in Evolutionary Composition.- Composing Using Heterogeneous Cellular Automata.- On the Socialization of Evolutionary Art.- An Evolutionary Music Composer Algorithm for Bass Harmonization.- Generation of Pop-Rock Chord Sequences Using Genetic Algorithms and Variable Neighborhood Search.- Elevated Pitch: Automated Grammatical Evolution of Short Compositions.- A GA-Based Control Strategy to Create Music with a Chaotic System.- Teaching Evolutionary Design Systems by Extending "Context Free".- Artificial Nature: Immersive World Making.- Evolving Indirectly Represented Melodies with Corpus-Based Fitness Evaluation.- Hearing Thinking.- EvoNUM Contributions.- Memetic Variation Local Search vs. Life-Time Learning in Electrical Impedance Tomography.- Estimating HMM Parameters Using Particle Swarm Optimisation.- Modeling Pheromone Dispensers Using Genetic Programming.- NK Landscapes Difficulty and Negative Slope Coefficient: How Sampling Influences the Results.- On the Parallel Speed-Up of Estimation of Multivariate Normal Algorithm and Evolution Strategies.- Adaptability of Algorithms for Real-Valued Optimization.- A Stigmergy-Based Algorithm for Continuous Optimization Tested on Real-Life-Like Environment.- Stochastic Local Search Techniques with Unimodal Continuous Distributions: A Survey.- Evolutionary Optimization Guided by Entropy-Based Discretization.- EvoSTOC Contributions.- The Influence of Population and Memory Sizes on the Evolutionary Algorithm's Performance for Dynamic Environments.- Differential Evolution with Noise Analyzer.- An Immune System Based Genetic Algorithm Using Permutation-Based Dualism for Dynamic Traveling Salesman Problems.- Dynamic Time-Linkage Problems Revisited.- The Dynamic Knapsack Problem Revisited: A New Benchmark Problem for Dynamic Combinatorial Optimisation.- Impact of Frequency and Severity on Non-Stationary Optimization Problems.- A Critical Look at Dynamic Multi-dimensional Knapsack Problem Generation.- EvoTRANSLOG Contributions.- Evolutionary Freight Transportation Planning.- An Effective Evolutionary Algorithm for the Cumulative Capacitated Vehicle Routing Problem.- A Corridor Method-Based Algorithm for the Pre-marshalling Problem.- Comparison of Metaheuristic Approaches for Multi-objective Simulation-Based Optimization in Supply Chain Inventory Management.- Heuristic Algorithm for Coordination in Public Transport under Disruptions.- Optimal Co-evolutionary Strategies for the Competitive Maritime Network Design Problem.
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The year 2009 celebrates the bicentenary of Darwin's birth and the 150th - niversary of the publication of his seminal work, On the Origin of Species.If this makes 2009 a special year for the research community working in biology and evolution, the ?eld of evolutionary computation (EC) also shares the same excitement. EC techniques are e?cient, nature-inspired planning and optimi- tion methods based on the principles of natural evolution and genetics. Due to their e?ciency and simple underlying principles, these methods can be used in the context of problem solving, optimization, and machine learning. A large and ever-increasing number of researchers and professionals make use of EC te- niques in various application domains. ThisvolumepresentsacarefulselectionofrelevantECapplicationscombi ned with a thorough examination of the techniques used in EC. The papers in the volume illustrate the current state of the art in the application of EC and can help and inspire researchers and professionals to develop e?cient EC methods for design and problem solving.
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• An introduction to evolutionary computation
• evolutionary algorithms as search algorithms
• theoretical analysis of evolutionary algorithms
• advanced search operators in evolutionary algorithms
• parallel evolutionary algorithms
• a comparison of simulated annealing and an evolutionary algorithm on traveling salesman problems
• power system design and management by evolutionary algorithms
• telecommunications network design and management by evolutionary algorithms
• an optimization tool based on evolutionary algorithms
• the evolution of artificial neural network architectures
• an experimental study of generalization in evolutionary learning
• an evolutionary approach to the N-person prisoner's dilemma game
• automated design and generalisation of heuristics
• high-order credit assignment in classifier systems.
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Evolutionary computation is the study of computational systems which use ideas and get inspiration from natural evolution and adaptation. This book is devoted to the theory and application of evolutionary computation. It is a self-contained volume which covers both introductory material and selected advanced topics. The book can roughly be divided into two major parts: the introductory one and the one on selected advanced topics. Each part consists of several chapters which present an in-depth discussion of selected topics. A strong connection is established between evolutionary algorithms and traditional search algorithms. This connection enables us to incorporate ideas in more established fields into evolutionary algorithms. The book is aimed at a wide range of readers. It does not require previous exposure to the field since introductory material is included. It will be of interest to anyone who is interested in adaptive optimization and learning. People in computer science, artificial intelligence, operations research, and various engineering fields will find it particularly interesting.
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• Methodology.- Memetic Algorithms in Planning, Scheduling, and Timetabling.- Landscapes, Embedded Paths and Evolutionary Scheduling.- Classical and Non-Classical Models of Production Scheduling.- Scheduling of Flow-Shop, Job-Shop, and Combined Scheduling Problems using MOEAs with Fixed and Variable Length Chromosomes.- Designing Dispatching Rules to Minimize Total Tardiness.- A Robust Meta-Hyper-Heuristic Approach to Hybrid Flow-Shop Scheduling.- Hybrid Particle Swarm Optimizers in the Single Machine Scheduling Problem: An Experimental Study.- An Evolutionary Approach for Solving the Multi-Objective Job-Shop Scheduling Problem.- Timetabling.- Multi-Objective Evolutionary Algorithm for University Class Timetabling Problem.- Metaheuristics for University Course Timetabling.- Energy Applications.- Optimum Oil Production Planning using an Evolutionary Approach.- A Hybrid Evolutionary Algorithm for Service Restoration in Power Distribution Systems.- Particle Swarm Optimisation for Operational Planning: Unit Commitment and Economic Dispatch.- Evolutionary Generator Maintenance Scheduling in Power Systems.- Networks.- Evolvable Fuzzy Scheduling Scheme for Multiple-ChannelPacket Switching Network.- A Multi-Objective Evolutionary Algorithm for Channel Routing Problems.- Transport.- Simultaneous Planning and Scheduling for Multi-Autonomous Vehicles.- Scheduling Production and Distribution of Rapidly Perishable Materials with Hybrid GA's.- A Scenario-based Evolutionary Scheduling Approach for Assessing Future Supply Chain Fleet Capabilities.- Business.- Evolutionary Optimization of Business Process Designs.- Using a Large Set of Low Level Heuristics in a Hyperheuristic Approach to Personnel Scheduling.- A Genetic-Algorithm-Based Reconfigurable Scheduler.- Evolutionary Algorithm for an Inventory Location Problem.
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Evolutionary scheduling is a vital research domain at the interface of artificial intelligence and operational research. This edited book gives an overview of many of the current developments in the large and growing field of evolutionary scheduling. It demonstrates the applicability of evolutionary computational techniques to solve scheduling problems, not only to small-scale test problems, but also fully-fledged real-world problems.
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• I: Foundations of Evolutionary Computation.- Evolutionary Algorithms.- Self-Adaptation.- II: Self-Adaptive Operators.- Biased Mutation for Evolution Strategies.- Self-Adaptive Inversion Mutation.- Self-Adaptive Crossover.- III: Constraint Handling.- Constraint Handling Heuristics for Evolution Strategies.- IV: Summary.- Summary and Conclusion.- V: Appendix.- Continuous Benchmark Functions.- Discrete Benchmark Functions.
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Evolutionary algorithms are successful biologically inspired meta-heuristics. Their success depends on adequate parameter settings. The question arises: how can evolutionary algorithms learn parameters automatically during the optimization? Evolution strategies gave an answer decades ago: self-adaptation. Their self-adaptive mutation control turned out to be exceptionally successful. But nevertheless self-adaptation has not achieved the attention it deserves. This book introduces various types of self-adaptive parameters for evolutionary computation. Biased mutation for evolution strategies is useful for constrained search spaces. Self-adaptive inversion mutation accelerates the search on combinatorial TSP-like problems. After the analysis of self-adaptive crossover operators the book concentrates on premature convergence of self-adaptive mutation control at the constraint boundary. Besides extensive experiments, statistical tests and some theoretical investigations enrich the analysis of the proposed concepts.
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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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• 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.
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This book gathers papers presented at the 13th International Conference on Genetic and Evolutionary Computing (ICGEC 2019), which was held in Qingdao, China, from 1st to 3rd, November 2019. Since it was established, in 2006, the ICGEC conference series has been devoted to new approaches with a focus on evolutionary computing. Today, it is a forum for the researchers and professionals in all areas of computational intelligence including evolutionary computing, machine learning, soft computing, data mining, multimedia and signal processing, swarm intelligence and security. The book appeals to policymakers, academics, educators, researchers in pedagogy and learning theory, school teachers, and other professionals in the learning industry, and further and continuing education. .
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• Ensemble Learning to Generate Virtual Data for Pattern Recognition and Its Possibility of Application to Strategy Inference in Table Tennis.- The rise of smart contract and its complex impacts on the digital ecosystem.- Automatic Collision Avoidance Using Deep Reinforcement Learning with Grid Sensor.- Two way allocation methods in SAVS for large depopulated area.- Proposal of mathematical model of prediction of human movement using position information.- Public transport smart card data analysis using tucker decomposition.- Estimating impressions for clothing, landscape, and indoor images using CNN.- Block-Based Neural Network High Speed Optimization.- An Improved Auto-encoder Based on 2-level Prioritized Experience Replay for High Dimension Skewed Data.- Multi-Task Learning Using Online Fine-Tuning Considering the Importance of Each Filter.- Visualizing Deep Q-Learning to Understanding Behavior of Swarm Robotic System.- A Study of Upper Tropospheric Circulations over the Northern Hemisphere Prediction Using Multivariate Features by ConvLSTM.- Learning localization skills of soccer robot using by simulated omni-vision camera.- Examination of analysis method of opinion distribution in News media transferred on Web.- Opinion distribution analysis of video media related to social life.- Quantitative consideration of spatio-temporal information on education and culture in Japan using telephone directory archive data.
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This book presents selected papers from the 23rd Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2019), which was held in Tottori, Japan, on December 6-8, 2019. Today, various types of intelligent system can be found everywhere. However, none of them can be developed or understood from only one perspective. As such, this book collects unique ways of thinking about intelligent systems. It discusses evolutionary and complex adaptive systems, which have been useful approaches for tackling intelligent systems. It also examines the recent developments in the field of artificial intelligence that are driving research on intelligent systems. Addressing topics related to intelligent transport systems, machine learning and neural networks, data science and decision analytics, evolutionary and nature-inspired computation, and agents and complex systems, this book is a valuable resource for researchers and practitioners wanting to develop or understand intelligent and evolutionary systems. .
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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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• Evolution of Analog Circuits
• Unconstrained Evolution of Analogue Computational "QR" Circuit with Oscillating Length Representation
• ISCLEs: Importance Sampled Circuit Learning Ensembles for Trustworthy Analog Circuit Topology Synthesis
• Evolution of Digital Circuits
• A Comparison of Evolvable Hardware Architectures for Classification Tasks
• Hardware Acceleration of an Immune Network Inspired Evolutionary Algorithm for Medical Diagnosis
• A Stepwise Dimension Reduction Approach to Evolutionary Design of Relative Large Combinational Logic Circuits
• Hardware-Software Codesign and Platforms for Adaptive Systems
• Evolutionary Graph Models with Dynamic Topologies on the Ubichip
• A Hardware-Software Design Framework for Distributed Cellular Computing
• Hardware/Software Co-synthesis of Distributed Embedded Systems Using Genetic Programming
• Self-Adaptive Networked Entities for Building Pervasive Computing Architectures
• Best Paper Award Competition
• Cellular Automata-Based Development of Combinational and Polymorphic Circuits: A Comparative Study
• Investigating the Suitability of FPAAs for Evolved Hardware Spiking Neural Networks
• The Segmental-Transmission-Line: Its Design and Prototype Evaluation
• On Evolutionary Synthesis of Linear Transforms in FPGA
• Evolutionary Robotics
• Towards Efficient Evolutionary Design of Autonomous Robots
• Indirect Online Evolution
• A Conceptual Framework for Adaptation in Industrial Robotic Systems
• Development
• A Developmental Gene Regulation Network for Constructing Electronic Circuits
• Discovery and Investigation of Inherent Scalability in Developmental Genomes
• Learning General Solutions through Multiple Evaluations during Development
• Real-World Applications
• Evolving MEMS Resonator Designs for Fabrication
• Self-Reconfigurable Mixed-Signal Integrated Circuits Architecture Comprising a Field Programmable Analog Array and a General Purpose Genetic Algorithm IP Core
• Evolutionary Networking
• Proposal for LDPC Code Design System Using Multi-Objective Optimization and FPGA-Based Emulation
• Scalability of a Novel Shifting Balance Theory-Based Optimization Algorithm: A Comparative Study on a Cluster-Based Wireless Sensor Network
• Evolutionary Design of Fault Tolerant Collective Communications
• Evolvable Artificial Neural Networks
• A Cellular Structure for Online Routing of Digital Spiking Neuron Axons and Dendrites on FPGAs
• Bio-inspired Event Coded Configurable Analog Circuit Block
• Dynamics of Firing Patterns in Evolvable Hierarchically Organized Neural Networks
• Transistor-Level Circuit Evolution
• Evolving Variability-Tolerant CMOS Designs
• Transistor-Level Evolution of Digital Circuits Using a Special Circuit Simulator
• Extended Posters
• Optimised State Assignment for FSMs Using Quantum Inspired Evolutionary Algorithm
• Evolvable Hardware: A Tool for Reverse Engineering of Biological Systems
• Coevolution of Neuro-developmental Programs That Play Checkers
• Hippocampus-Inspired Spiking Neural Network on FPGA
• Fault-Tolerant Memory Design and Partitioning Issues in Embryonics
• The Input Pattern Order Problem: Evolution of Combinatorial and Sequential Circuits in Hardware
• Neural Development on the Ubichip by Means of Dynamic Routing Mechanisms
• Short Posters
• The Perplexus Programming Framework: Combining Bio-inspiration and Agent-Oriented Programming for the Simulation of Large Scale Complex Systems
• Quantum Bio-inspired Vision Model on System-on-a-Chip (SoC)
• Evolutionary Meta Compilation: Evolving Programs Using Real World Engineering Tools
• Waveguide Synthesis by Genetic Algorithms with Multiple Crossover
• Parallel Grammatical Evolution for Circuit Optimization
• Self-organization of Bio-inspired Integrated Circuits
• Artificial Creativity in Linguistics Using Evolvable Fuzzy Neural Networks.

This book constitutes the refereed proceedings of the 8th International Conference on Evolvable Systems, ICES 2008, held in Prague, Czech Republic, in September 2008. The 28 revised full papers and 14 revised poster papers presented were carefully reviewed and selected from 52 submissions. The papers are organized in topical sections on evolution of analog circuits, evolution of digital circuits, hardware-software codesign and platforms for adaptive systems, evolutionary robotics, development, real-world applications, evolutionary networking, evolvable artificial neural networks, and transistor-level circuit evolution.
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• Continuous Constrained Optimization with Dynamic Tolerance Using the COPSO Algorithm.- Boundary Search for Constrained Numerical Optimization Problems.- Solving Difficult Constrained Optimization Problems by the ? Constrained Differential Evolution with Gradient-Based Mutation.- Constrained Real-Parameter Optimization with ? -Self-Adaptive Differential Evolution.- Self-adaptive and Deterministic Parameter Control in Differential Evolution for Constrained Optimization.- An Adaptive Penalty Function for Handling Constraint in Multi-objective Evolutionary Optimization.- Infeasibility Driven Evolutionary Algorithm for Constrained Optimization.- On GA-AIS Hybrids for Constrained Optimization Problems in Engineering.- Constrained Optimization Based on Quadratic Approximations in Genetic Algorithms.- Constraint-Handling in Evolutionary Aerodynamic Design.- Handling Constraints in Global Optimization Using Artificial Immune Systems: A Survey.
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Evolutionary algorithms (EAs), as well as other bio-inspired heuristics, are widely usedto solvenumericaloptimizationproblems.However, intheir or- inal versions, they are limited to unconstrained search spaces i.e they do not include a mechanism to incorporate feasibility information into the ?tness function. On the other hand, real-world problems usually have constraints in their models. Therefore, a considerable amount of research has been d- icated to design and implement constraint-handling techniques. The use of (exterior) penalty functions is one of the most popular methods to deal with constrained search spaces when using EAs. However, other alternative me- ods have been proposed such as: special encodings and operators, decoders, the use of multiobjective concepts, among others. An e?cient and adequate constraint-handling technique is a key element in the design of competitive evolutionary algorithms to solve complex op- mization problems. In this way, this subject deserves special research e?orts. After asuccessfulspecialsessiononconstraint-handlingtechniquesusedin evolutionary algorithms within the Congress on Evolutionary Computation (CEC) in 2007, and motivated by the kind invitation made by Dr. Janusz Kacprzyk, I decided to edit a book, with the aim of putting together recent studies on constrained numerical optimization using evolutionary algorithms and other bio-inspired approaches. The intended audience for this book comprises graduate students, prac- tionersandresearchersinterestedonalternativetechniquestosolvenum erical optimization problems in presence of constraints.
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• Agent Based Evolutionary Approach: An Introduction
• Multi-Agent Evolutionary Model for Global Numerical Optimization
• An Agent Based Evolutionary Approach for Nonlinear Optimization with Equality Constraints
• Multiagent-Based Approach for Risk Analysis in Mission Capability Planning
• Agent Based Evolutionary Dynamic Optimization
• Divide and Conquer in Coevolution: A Difficult Balancing Act
• Complex Emergent Behaviour from Evolutionary Spatial Animat Agents
• An Agent-Based Parallel Ant Algorithm with an Adaptive Migration Controller
• An Attempt to Stochastic Modeling of Memetic Systems
• Searching for the Effective Bidding Strategy Using Parameter Tuning in Genetic Algorithm
• PSO (Particle Swarm Optimization): One Method, Many Possible Applications
• VISPLORE: Exploring Particle Swarms by Visual Inspection.

Presenting state-of-the-art theory and practice in the field, this volume includes novel frameworks, a convergence and complexity analysis, and real-world applications. It aims to raise awareness of this effective method of enhancing evolutionary algorithms.

• A New Local Search Algorithm for the DNA Fragment Assembly Problem.- A Hybrid Immune-Based System for the Protein Folding Problem.- A Genetic Algorithm for the Resource Renting Problem with Minimum and Maximum Time Lags.- A Probabilistic Beam Search Approach to the Shortest Common Supersequence Problem.- Genetic Algorithms for Word Problems in Partially Commutative Groups.- A GRASP and Branch-and-Bound Metaheuristic for the Job-Shop Scheduling.- Reducing the Size of Traveling Salesman Problem Instances by Fixing Edges.- Iterated k-Opt Local Search for the Maximum Clique Problem.- Accelerating Local Search in a Memetic Algorithm for the Capacitated Vehicle Routing Problem.- Evolutionary Algorithms for Real-World Instances of the Automatic Frequency Planning Problem in GSM Networks.- A New Metaheuristic for the Vehicle Routing Problem with Split Demands.- Generation of Tree Decompositions by Iterated Local Search.- Edge Assembly Crossover for the Capacitated Vehicle Routing Problem.- Tackling the Container Loading Problem: A Hybrid Approach Based on Integer Linear Programming and Genetic Algorithms.- A Population-Based Local Search for Solving a Bi-objective Vehicle Routing Problem.- Combining Lagrangian Decomposition with an Evolutionary Algorithm for the Knapsack Constrained Maximum Spanning Tree Problem.- Exact/Heuristic Hybrids Using rVNS and Hyperheuristics for Workforce Scheduling.- An Analysis of Problem Difficulty for a Class of Optimisation Heuristics.- A New Grouping Genetic Algorithm for the Quadratic Multiple Knapsack Problem.- A Hybrid Method for Solving Large-Scale Supply Chain Problems.- Crossover Operators for the Car Sequencing Problem.
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This book constitutes the refereed proceedings of the 7th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2007, held in Valencia, Spain in April 2007. The 21 revised full papers cover evolutionary algorithms as well as various other metaheuristics, like scatter search, tabu search, memetic algorithms, variable neighborhood search, ant colony optimization, and particle swarm optimization algorithms.
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• Part 1 Introduction
• Part 2 Reinforcement Learning
• Part 3 On-Line Evolutionary Computation
• Part 4 Evolutionary Function Approximation
• Part 5 Sample-Efficient Evolutionary Function Approximation
• Part 6 Automatic Feature Selection for Reinforcement Learning
• Part 7 Adaptive Tile Coding
• Part 8 RelatedWork
• Part 9 Conclusion
• Part 10 Statistical Significance.

This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own representations have the potential to dramatically improve performance. This book introduces two novel approaches for automatically discovering high-performing representations. The first approach synthesizes temporal difference methods, the traditional approach to reinforcement learning, with evolutionary methods, which can learn representations for a broad class of optimization problems. This synthesis is accomplished by customizing evolutionary methods to the on-line nature of reinforcement learning and using them to evolve representations for value function approximators. The second approach automatically learns representations based on piecewise-constant approximations of value functions. It begins with coarse representations and gradually refines them during learning, analyzing the current policy and value function to deduce the best refinements. This book also introduces a novel method for devising input representations. This method addresses the feature selection problem by extending an algorithm that evolves the topology and weights of neural networks such that it evolves their inputs too. In addition to introducing these new methods, this book presents extensive empirical results in multiple domains demonstrating that these techniques can substantially improve performance over methods with manual representations.

• Section I Introduction.- Why Is Optimization Difficult?.- The Rationale behind Seeking Inspiration from Nature.-
• Section II Evolutionary Intelligence.- The Evolutionary-Gradient-Search Procedure in Theory and Practice.- The Evolutionary Transition Algorithm: Evolving Complex Solutions out of Simpler Ones.- A Model-Assisted Memetic Algorithm for Expensive Optimization Problems.- A Self-Adaptive Mixed Distribution Based Uni-variate Estimation of Distribution Algorithm for Large Scale Global Optimization.- Differential Evolution with Fitness Diversity Self-Adaptation.- Central Pattern Generators: Optimisation and Application.-
• Section III Collective Intelligence.- Fish School Search.- Magnifier Particle Swarm Optimization.- Improved Particle Swarm Optimization in Constrained Numerical Search Spaces.- Applying River Formation Dynamics to Solve NP-Complete Problems.-
• Section IV Social-Natural Intelligence.- Algorithms Inspired in Social Phenomena.- Artificial Immune Systems for Optimization.-
• Section V Multi-Objective Optimisation.- Ranking Methods in Many-objective Evolutionary Algorithms.- On the Effect of Applying a Steady-State Selection Scheme in the Multi-Objective Genetic Algorithm NSGA-II.- Improving the Performance of Multiobjective Evolutionary Optimization Algorithms using Coevolutionary Learning.- Evolutionary Optimization for Multiobjective Portfolio Selection Under Markowitz's Model with Application to the Caracas Stock Exchange.
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Nature-Inspired Algorithms are gaining much popularity in recent years due to the fact that many real-world optimisation problems have become increasingly large, complex and dynamic. The size and complexity of the problems nowadays require the development of methods and solutions whose efficiency is measured by their ability to find acceptable results within a reasonable amount of time, rather than an ability to guarantee the optimal solution. This volume "Nature-Inspired Algorithms for Optimisation" is a collection of the latest state-of-the-art algorithms and important studies for tackling various kinds of optimisation problems. It comprises 18 chapters, including two introductory chapters which address the fundamental issues that have made optimisation problems difficult to solve and explain the rationale for seeking inspiration from nature. The contributions stand out through their novelty and clarity of the algorithmic descriptions and analyses, and lead the way to interesting and varied new applications.
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• Agents and Complex Systems.- Data Mining and Its Applications.- Evolutionary Algorithms and Optimization.- Intelligent Systems and their Applications.- Nature Inspired Creative Computing.- Smart Workspace and Image Processing.
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This PALO volume constitutes the Proceedings of the 19th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2015), held in Bangkok, Thailand, November 22-25, 2015. The IES series of conference is an annual event that was initiated back in 1997 in Canberra, Australia. IES aims to bring together researchers from countries of the Asian Pacific Rim, in the fields of intelligent systems and evolutionary computation, to exchange ideas, present recent results and discuss possible collaborations. Researchers beyond Asian Pacific Rim countries are also welcome and encouraged to participate. The theme for IES 2015 is "Transforming Big Data into Knowledge and Technological Breakthroughs". The host organization for IES 2015 is the School of Information Technology (SIT), King Mongkut's University of Technology Thonburi (KMUTT), and it is technically sponsored by the International Neural Network Society (INNS). IES 2015 is collocated with three other conferences; namely, The 6th International Conference on Computational Systems-Biology and Bioinformatics 2015 (CSBio 2015), The 7th International Conference on Advances in Information Technology 2015 (IAIT 2015) and The 10th International Conference on e-Business 2015 (iNCEB 2015), as a major part of series of events to celebrate the SIT 20th anniversary and the KMUTT 55th anniversary.
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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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• Diffusion of Shapes (R S Shaw & N H Packard)
• FDC-Based Particle Swarm Optimization (A Azzini et al.)
• Predictive Neural Networks in the Design of Experiments (D De March et al.)
• Prefrontal Cortex and Action Sequences: A Review on Neural Computational Models (I Gaudiello et al.)
• Bio-Inspired ICT for Evolutionary Emotional Intelligence (M Villamira & P Cipresso)
• Cooperation in Corvids: A Simulative Study with Evolved Robots (O Miglino et al.)
• Distributed Processes in a Agent-Based Model of Innovation (L Ansaloni et al.)
• Imaginary or Actual Artificial Worlds Using a New Tool in the ABM Perspective (P Terna)
• Dynamics of Interconnected Boolean Networks with Scale-Free Topology (C Damiani et al.)
• Semi-Synthetical Minimal Cells (P Stano & P L Luisi)
• and Other Papers.
• (source: Nielsen Book Data)

The Italian community in Artificial Life and Evolutionary computation has grown remarkably in recent years, and this book is the first broad collection of its major interests and achievements (including contributions from foreign countries). The contributions in Artificial Life as well as in Evolutionary Computation allow one to see the deep connections between the two fields. The topics addressed are extremely relevant for present day research in Artificial Life and in Evolutionary Computation, which include important contributions from very well-known researchers. The volume provides a very broad picture of the Italian activities in this field.
(source: Nielsen Book Data)

• Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews.- Quantum-Inspired Evolutionary Algorithm for Numerical Optimization.- Enhanced Evolutionary Algorithms for Multidisciplinary Design Optimization: A Control Engineering Perspective.- Hybrid Evolutionary Algorithms and Clustering Search.- A Novel Hybrid Algorithm for Function Optimization: Particle Swarm Assisted Incremental Evolution Strategy.- An Efficient Nearest Neighbor Classifier.- Hybrid Genetic: Particle Swarm Optimization Algorithm.- A Hybrid Genetic Algorithm and Bacterial Foraging Approach for Global Optimization and Robust Tuning of PID Controller with Disturbance Rejection.- Memetic Algorithms Parametric Optimization for Microlithography.- Significance of Hybrid Evolutionary Computation for Ab Initio Protein Folding Prediction.- A Hybrid Evolutionary Heuristic for Job Scheduling on Computational Grids.- Clustering Gene-Expression Data: A Hybrid Approach that Iterates Between k-Means and Evolutionary Search.- Robust Parametric Image Registration.- Pareto Evolutionary Algorithm Hybridized with Local Search for Biobjective TSP.
• (source: Nielsen Book Data)

This edited volume is targeted at presenting the latest state-of-the-art methodologies in "Hybrid Evolutionary Algorithms". The chapters deal with the theoretical and methodological aspects, as well as various applications to many real world problems from science, technology, business or commerce. Overall, the book has 14 chapters including an introductory chapter giving the fundamental definitions and some important research challenges. The contributions were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed.
(source: Nielsen Book Data)

• Theory.- Extremal Optimization Dynamics in Neutral Landscapes: The Royal Road Case.- Improving the Scalability of EA Techniques: A Case Study in Clustering.- Ant Colony Optimization.- MC-ANT: A Multi-Colony Ant Algorithm.- Applications.- Artificial Evolution for 3D PET Reconstruction.- A Hybrid Genetic Algorithm/Variable Neighborhood Search Approach to Maximizing Residual Bandwidth of Links for Route Planning.- Parallelization of an Evolutionary Algorithm on a Platform with Multi-core Processors.- On the Difficulty of Inferring Gene Regulatory Networks: A Study of the Fitness Landscape Generated by Relative Squared Error.- Combinatorial Optimization.- Memetic Algorithms for Constructing Binary Covering Arrays of Strength Three.- A Priori Knowledge Integration in Evolutionary Optimization.- Robotics.- On-Line, On-Board Evolution of Robot Controllers.- The Transfer of Evolved Artificial Immune System Behaviours between Small and Large Scale Robotic Platforms.- Multi-objective Optimization.- An Analysis of Algorithmic Components for Multiobjective Ant Colony Optimization: A Case Study on the Biobjective TSP.- Alternative Fitness Assignment Methods for Many-Objective Optimization Problems.- Genetic Programming.- Evolving Efficient List Search Algorithms.- Semantic Similarity Based Crossover in GP: The Case for Real-Valued Function Regression.- Genetic-Programming Based Prediction of Data Compression Saving.- Machine Learning.- On the Characteristics of Sequential Decision Problems and Their Impact on Evolutionary Computation and Reinforcement Learning.
• (source: Nielsen Book Data)

This LNCS volume contains the best papers presented at the 9th Conference on 1 Arti?cial Evolution, EA 2009, held in Strasbourg (France). Previous EA events took place in Tours (2007, LNCS 4926), Lille (2005, LNCS 3871), Marseille (2003, LNCS 2936), Le Creusot (2001, LNCS 2310), Dunkerque (1999, LNCS 1829), Nimes (1997, LNCS 1363), Brest (1995, LNCS 1063) and Toulouse (1994, LNCS 1063). For this ninth edition, authors were invited to present their originalwork r- evantto arti?cialevolution, including, butnotlimitedto:evolutionarycompu- tion, evolutionaryoptimization, co-evolution, arti?ciallife, populationdynamics, theory, algorithmicsand modeling, implementations, application of evolutionary paradigmstotherealworld(industry, biosciences, ...), otherbiologically-inspired paradigms (swarm, arti?cial ants, arti?cial immune systems, ...), memetic al- rithms, multi-objective optimization, constraint handling, parallel algorithms, dynamic optimization, machine learning and hybridization with other soft c- puting techniques. Submitted papers were reviewed by at least four members of the Inter- tional ProgramCommittee, which selected 23 papers to be presented during the conference out of 43 submissions. However, only 17 papers were included in the present volume, resulting in a 39.5% acceptance rate. We would like to thank the members of the Program Committee for their conscientious work, the authors for their greatly appreciated contributions, but also the members of the Organizing Committee who, once more, managed to put together a really enjoyable conference in Strasbourg. Finally, ?nancial and material support coming from ENSPS, Universit'ede Strasbourg, CNRS, R' egion Alsace and the EA association contributed to the success of the conference and helped to keep registration fees very low.
(source: Nielsen Book Data)

• EvoBAFIN: Enhanced Multiobjective Population-Based Incremental Learning with Applications in Risk Treaty Optimization
• Genetic Programming with Memory for Financial Trading
• Improving Fitness Functions in Genetic Programming for Classification on Unbalanced Credit Card Data
• Evolving Classification Models for Prediction of Patient Recruitment in Multicentre Clinical Trials Using Grammatical Evolution
• Portfolio Optimization, a Decision-Support Methodology for Small Budgets
• Evolutionary Multiobjective Optimization for Portfolios in Emerging Markets: Contrasting Higher Moments and Median Models
• EvoBIO: On Combinatorial Optimisation in Analysis of Protein-Protein Interaction and Protein Folding Networks
• A Multi-objective Genetic Programming Biomarker Detection Approach in Mass Spectrometry Data
• Automating Biomedical Data Science Through Tree-Based Pipeline Optimization
• Bicliques in Graphs with Correlated Edges: From Artificial to Biological Networks
• Hybrid Biclustering Algorithms for Data Mining
• Discovering Potential Clinical Profiles of Multiple Sclerosis from Clinical and Pathological Free Text Data with Constraint Non-negative Matrix Factorization
• Application of Evolutionary Algorithms for the Optimization of Genetic Regulatory Networks
• EvoCOMNET: A Hybrid Discrete Artificial Bee Colony Algorithm for the Multicast Routing Problem
• Evolving Coverage Optimisation Functions for Heterogeneous Networks Using Grammatical Genetic Programming
• Joint Topology Optimization, Power Control and Spectrum Allocation for Intra-Vehicular Multi-hop Sensor Networks Using Dandelion-Encoded Heuristics
• A Heuristic Crossover Enhanced Evolutionary Algorithm for Clustering Wireless Sensor Network
• A Variable Local Search Based Memetic Algorithm for the Load Balancing Problem in Cloud Computing
• An (MI)LP-Based Primal Heuristic for 3-Architecture Connected Facility Location in Urban Access Network Design
• Reducing Efficiency of Connectivity-Splitting Attack on Newscast via Limited Gossip
• A Distributed Intrusion Detection Framework Based on Evolved Specialized Ensembles of Classifiers
• UAV Fleet Mobility Model with Multiple Pheromones for Tracking Moving Observation Targets
• EvoCOMPLEX: Towards Intelligent Biological Control: Controlling Boolean Networks with Boolean Networks
• The Emergence of Cooperation in Public Goods Games on Randomly Growing Dynamic Networks
• Influence Maximization in Social Networks with Genetic Algorithms
• Measuring Diversity of Socio-Cognitively Inspired ACO Search
• Multiwinner Voting in Genetic Algorithms for Solving Ill-Posed Global Optimization Problems
• EvoENERGY: A Decentralized PSO with Decoder for Scheduling Distributed Electricity Generation
• Comparison of Multi-objective Evolutionary Optimization in Smart Building Scenarios
• Stigmergy-Based Scheduling of Flexible Loads
• Electrical Load Pattern Shape Clustering Using Ant Colony Optimization
• Optimization of Operation and Control Strategies for Battery Energy Storage Systems by Evolutionary Algorithms
• EvoGAMES: Orthogonally Evolved AI to Improve Difficulty Adjustment in Video Games
• There Can Be only One: Evolving RTS Bots via Joust Selection
• Constrained Level Generation through Grammar-Based Evolutionary Algorithms
• Evolving Chess-Like Games Using Relative Algorithm Performance Profiles
• Online Evolution for Multi-action Adversarial Games
• The Story of Their Lives: Massive Procedural Generation of Heroes' Journeys Using Evolved Agent-Based Models and Logical Reasoning
• Dangerousness Metric for Gene Regulated Car Driving
• Using Isovists to Evolve Terrains with Gameplay Elements
• A Spatially-Structured PCG Method for Content Diversity in a Physics-Based simulation Game
• Design and Evaluation of an Extended Learning Classifier-Based StarCraft Micro AI
• EvoIASP: A Wrapper Feature Selection Approach to Classification with Missing Data
• Bare-Bone Particle Swarm Optimisation for Simultaneously Discretising and Selecting Features For High-Dimensional Classification
• Mutual Information Estimation for Filter Based Feature Selection Using Particle Swarm Optimization
• Speaker Verification on Unbalanced Data with Genetic Programming
• Binary Tomography Reconstruction by Particle Aggregation
• Population Based Ant Colony Optimization for Reconstructing ECG Signals
• EvoINDUSTRY: Can Evolutionary Algorithms Beat Dynamic Programming for Hybrid
• NSGA-II Based Auto-Calibration of Automatic Number Plate Recognition Camera for Vehicle Speed Measurement
• Environment-Model Based Testing with Differential Evolution in an Industrial Setting
• Workforce Scheduling in Inbound Customer Call Centres with a Case Study.

The two volumes LNCS 9597 and 9598 constitute the refereed conference proceedings of the 19th European Conference on the Applications of Evolutionary Computation, EvoApplications 2016, held in Porto, Portugal, in March/April 2016, co-located with the Evo* 2016 events EuroGP, EvoCOP, and EvoMUSART. The 57 revised full papers presented together with 17 poster papers were carefully reviewed and selected from 115 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), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for risk management, security and defence applications), EvoROBOT (evolutionary robotics), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).