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

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

• Introduction 1.1 Artificial neural networks and deep learning
• 1.2 Evolutionary optimization and learning
• 1.3 Privacy-preserving computation
• 1.4 Federated learning
• 1.5 Summary
• Communication-Efficient
• Federated Learning 2.1 Communication cost in federated learning
• 2.2 Main methodologies
• 2.3 Temporally weighted averaging and layer-wise weight update
• 2.4 Trained ternary compression for federated learning
• 2.5 Summary
• Evolutionary
• Federated Learning
• 3.1 Motivations and challenges
• 3.2 Offline evolutionary multi-objective federated learning
• 3.3 Realtime evolutionary federated neural architecture search
• 3.4 Summary
• Secure
• Federated Learning
• 4.1 Threats to federated learning
• 4.2 Distributed encryption for horizontal federated learning
• 4.3 Secure vertical federated learning
• 4.4 Summary
• Summary
• and Outlook
• 5.1 Summary
• 5.2 Future directions.
• (source: Nielsen Book Data)

This book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory, privacy, and security requirements. The book starts with a self-contained introduction to artificial neural networks, deep learning models, supervised learning algorithms, evolutionary algorithms, and evolutionary learning. Concise information is then presented on multi-party secure computation, differential privacy, and homomorphic encryption, followed by a detailed description of federated learning. In turn, the book addresses the latest advances in federate learning research, especially from the perspectives of communication efficiency, evolutionary learning, and privacy preservation. The book is particularly well suited for graduate students, academic researchers, and industrial practitioners in the field of machine learning and artificial intelligence. It can also be used as a self-learning resource for readers with a science or engineering background, or as a reference text for graduate courses. .
(source: Nielsen Book Data)

• 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.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• Induction of Fuzzy Rules by Means of Artificial Immune Systems in Bioinformatics.- Fuzzy Genome Sequence Assembly for Single and Environmental Genomes.- A Hybrid Promoter Analysis Methodology for Prokaryotic Genomes.- Fuzzy Vector Filters for cDNA Microarray Image Processing.- Microarray Data Analysis Using Fuzzy Clustering Algorithms.- Fuzzy Patterns and GCS Networks to Clustering Gene Expression Data.- Gene Expression Analysis by Fuzzy and Hybrid Fuzzy Classification.- Detecting Gene Regulatory Networks from Microarray Data using Fuzzy Logic.- Fuzzy System Methods in Modeling Gene Expression and Analyzing Protein Networks.- Evolving a Fuzzy Rulebase to Model Gene Expression.- Infer Genetic / Transcriptional Regulatory Networks by Recognition of Microarray Gene Expression Patterns using Adaptive Neuro-Fuzzy Inference Systems.- Scalable Dynamic Fuzzy Biomolecular Network Models for Large Scale Biology.- Fuzzy C-means Techniques for Medical Image Segmentation.- Monitoring and Control of Anesthesia Using Multivariable Self-Organizing Fuzzy Logic Structure.- Interval Type-2 Fuzzy System for ECG Arrhythmic Classification.- Fuzzy Logic in Evolving in silico Oscillatory Dynamics for Gene Regulatory Networks.
• (source: Nielsen Book Data)

Biological systems are inherently stochastic and uncertain. Thus, research in bioinformatics, biomedical engineering and computational biology has to deal with a large amount of uncertainties. Fuzzy logic has shown to be a powerful tool in capturing different uncertainties in engineering systems. In recent years, fuzzy logic based modeling and analysis approaches are also becoming popular in analyzing biological data and modeling biological systems. Numerous research and application results have been reported that demonstrated the effectiveness of fuzzy logic in solving a wide range of biological problems found in bioinformatics, biomedical engineering, and computational biology. Contributed by leading experts world-wide, this edited book contains 16 chapters presenting representative research results on the application of fuzzy systems to genome sequence assembly, gene expression analysis, promoter analysis, cis-regulation logic analysis and synthesis, reconstruction of genetic and cellular networks, as well as biomedical problems, such as medical image processing, electrocardiogram data classification and anesthesia monitoring and control. This volume is a valuable reference for researchers, practitioners, as well as graduate students working in the field of bioinformatics, biomedical engineering and computational biology.
(source: Nielsen Book Data)

• Multi-Objective Clustering, Feature Extraction and Feature Selection.- Feature Selection Using Rough Sets.- Multi-Objective Clustering and Cluster Validation.- Feature Selection for Ensembles Using the Multi-Objective Optimization Approach.- Feature Extraction Using Multi-Objective Genetic Programming.- Multi-Objective Learning for Accuracy Improvement.- Regression Error Characteristic Optimisation of Non-Linear Models.- Regularization for Parameter Identification Using Multi-Objective Optimization.- Multi-Objective Algorithms for Neural Networks Learning.- Generating Support Vector Machines Using Multi-Objective Optimization and Goal Programming.- Multi-Objective Optimization of Support Vector Machines.- Multi-Objective Evolutionary Algorithm for Radial Basis Function Neural Network Design.- Minimizing Structural Risk on Decision Tree Classification.- Multi-objective Learning Classifier Systems.- Multi-Objective Learning for Interpretability Improvement.- Simultaneous Generation of Accurate and Interpretable Neural Network Classifiers.- GA-Based Pareto Optimization for Rule Extraction from Neural Networks.- Agent Based Multi-Objective Approach to Generating Interpretable Fuzzy Systems.- Multi-objective Evolutionary Algorithm for Temporal Linguistic Rule Extraction.- Multiple Objective Learning for Constructing Interpretable Takagi-Sugeno Fuzzy Model.- Multi-Objective Ensemble Generation.- Pareto-Optimal Approaches to Neuro-Ensemble Learning.- Trade-Off Between Diversity and Accuracy in Ensemble Generation.- Cooperative Coevolution of Neural Networks and Ensembles of Neural Networks.- Multi-Objective Structure Selection for RBF Networks and Its Application to Nonlinear System Identification.- Fuzzy Ensemble Design through Multi-Objective Fuzzy Rule Selection.- Applications of Multi-Objective Machine Learning.- Multi-Objective Optimisation for Receiver Operating Characteristic Analysis.- Multi-Objective Design of Neuro-Fuzzy Controllers for Robot Behavior Coordination.- Fuzzy Tuning for the Docking Maneuver Controller of an Automated Guided Vehicle.- A Multi-Objective Genetic Algorithm for Learning Linguistic Persistent Queries in Text Retrieval Environments.- Multi-Objective Neural Network Optimization for Visual Object Detection.
• (source: Nielsen Book Data)

Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.
(source: Nielsen Book Data)

• Introduction
• A Selected Introduction to Evolutionary Computation / Xin Yao
• The Use of Collective Memory in Genetic Programming / Keith Bearpark, Andy J. Keane
• A Cultural Algorithm for Solving the Job Shop Scheduling Problem / Ricardo Landa Becerra, Carlos A. Coello Coello
• Case-Initialized Genetic Algorithms for Knowledge Extraction and Incorporation / Judy Johnson, Sushil J. Louis
• Using Cultural Algorithms to Evolve Strategies in A Complex Agent-based System / David A. Ostrowski, Robert G. Reynolds
• Methods for Using Surrogate Models to Speed Up Genetic Algorithm Optimization: Informed Operators and Genetic Engineering / Khaled Rasheed, Xiao Ni, Swaroop Vattam
• Fuzzy Knowledge Incorporation in Crossover and Mutation / Jun Zhang, Henry S.H. Chung, Alan W.L. Lo, B.J. Hu
• Learning Probabilistic Models for Enhanced Evolutionary Computation / Peter A.N. Bosman, Dirk Thierens.

• Probabilistic Models for Linkage Learning in Forest Management / Els I. Ducheyne, B. De Baets, R. De Wulf
• Performance-Based Computation of Chromosome Lifetimes in Genetic Algorithms / Adnan Acan, Yüce Tekol
• Genetic Algorithm and Case-Based Reasoning Applied in Production Scheduling / Pei-Chann Chang, Jih-Chang Hsieh, Yen-Wen Wang
• Knowledge-Based Evolutionary Search for Inductive Concept Learning / Federico Divina, Elena Marchiori
• An Evolutionary Algorithm with Tabu Restriction and Heuristic Reasoning for Multiobjective Optimization / E.F. Khor, K.C. Tan, Y.J. Yang
• Neural Networks for Fitness Approximation in Evolutionary Optimization / Yaochu Jin, Michael Hüsken, Markus Olhofer, Bernhard Sendhoff
• Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems / Yew Soon Ong, P.B. Nair, A.J. Keane, K.W. Wong
• Model Assisted Evolution Strategies / Holger Ulmer, Felix Streichert, Andreas Zell.

• Knowledge Incorporation Through Lifetime Learning / Kim W.C. Ku, M.W. Mak
• Local Search Direction for Multi-Objective Optimization Using Memetic EMO Algorithms / Tadahiko Murata, Shiori Kaige, Hisao Ishibuchi
• Fashion Design Using Interactive Genetic Algorithm with Knowledge-based Encoding / Hee-Su Kim, Sung-Bae Cho
• Interactive Evolutionary Design / Ian C. Parmee, Johnson A. Abraham
• Preference Incorporation in Multi-objective Evolutionary Computation
• Integrating User Preferences into Evolutionary Multi-Objective Optimization / Jürgen Branke, Kalyanmoy Deb
• Human Preferences and their Applications in Evolutionary Multi--Objective Optimization / Dragan Cvetković, Carlos A. Coello Coello
• An Interactive Fuzzy Satisficing Method for Multiobjective Integer Programming Problems through Genetic Algorithms / Kosuke Kato, Cahit Perkgoz, Masatoshi Sakawa
• Interactive Preference Incorporation in Evolutionary Engineering Design / Jiachuan Wang, Janis P. Terpenny.

Incorporation of a priori knowledge, such as expert knowledge, meta-heuristics and human preferences, as well as domain knowledge acquired during evolu- tionary search, into evolutionary algorithms has received increasing interest in the recent years. It has been shown from various motivations that knowl- edge incorporation into evolutionary search is able to significantly improve search efficiency. However, results on knowledge incorporation in evolution- ary computation have been scattered in a wide range of research areas and a systematic handling of this important topic in evolutionary computation still lacks. This edited book is a first attempt to put together the state-of-art and re- cent advances on knowledge incorporation in evolutionary computation within a unified framework. Existing methods for knowledge incorporation are di- vided into the following five categories according to the functionality of the incorporated knowledge in the evolutionary algorithms. 1. Knowledge incorporation in representation, population initialization, - combination and mutation. 2. Knowledge incorporation in selection and reproduction. 3. Knowledge incorporation in fitness evaluations. 4. Knowledge incorporation through life-time learning and human-computer interactions. 5. Incorporation of human preferences in multi-objective evolutionary com- putation. The intended readers of this book are graduate students, researchers and practitioners in all fields of science and engineering who are interested in evolutionary computation. The book is divided into six parts. Part I contains one introductory chapter titled "A selected introduction to evolutionary computation" by Yao, which presents a concise but insightful introduction to evolutionary computation.
(source: Nielsen Book Data)

• pt. 1. Self-organizing swarm robotic systems
• pt. 2. Self-reconfigurable modular robots
• pt. 3. Autonomous mental development in robotic systems
• pt. 4. Special applications.

Self-organizing approaches inspired from biological systems, such as social insects, genetic, molecular and cellular systems under morphogenesis, and human mental development, has enjoyed great success in advanced robotic systems that need to work in dynamic and changing environments. Compared with classical control methods for robotic systems, the major advantages of bio-inspired self-organizing robotic systems include robustness, self-repair and self-healing in the presence of system failures and/or malfunctions, high adaptability to environmental changes, and autonomous self-organization and self-reconfiguration without a centralized control. "Bio-inspired Self-organizing Robotic Systems" provides a valuable reference for scientists, practitioners and research students working on developing control algorithms for self-organizing engineered collective systems, such as swarm robotic systems, self-reconfigurable modular robots, smart material based robotic devices, unmanned aerial vehicles, and satellite constellations

• Multi-Objective Clustering, Feature Extraction and Feature Selection.- Feature Selection Using Rough Sets.- Multi-Objective Clustering and Cluster Validation.- Feature Selection for Ensembles Using the Multi-Objective Optimization Approach.- Feature Extraction Using Multi-Objective Genetic Programming.- Multi-Objective Learning for Accuracy Improvement.- Regression Error Characteristic Optimisation of Non-Linear Models.- Regularization for Parameter Identification Using Multi-Objective Optimization.- Multi-Objective Algorithms for Neural Networks Learning.- Generating Support Vector Machines Using Multi-Objective Optimization and Goal Programming.- Multi-Objective Optimization of Support Vector Machines.- Multi-Objective Evolutionary Algorithm for Radial Basis Function Neural Network Design.- Minimizing Structural Risk on Decision Tree Classification.- Multi-objective Learning Classifier Systems.- Multi-Objective Learning for Interpretability Improvement.- Simultaneous Generation of Accurate and Interpretable Neural Network Classifiers.- GA-Based Pareto Optimization for Rule Extraction from Neural Networks.- Agent Based Multi-Objective Approach to Generating Interpretable Fuzzy Systems.- Multi-objective Evolutionary Algorithm for Temporal Linguistic Rule Extraction.- Multiple Objective Learning for Constructing Interpretable Takagi-Sugeno Fuzzy Model.- Multi-Objective Ensemble Generation.- Pareto-Optimal Approaches to Neuro-Ensemble Learning.- Trade-Off Between Diversity and Accuracy in Ensemble Generation.- Cooperative Coevolution of Neural Networks and Ensembles of Neural Networks.- Multi-Objective Structure Selection for RBF Networks and Its Application to Nonlinear System Identification.- Fuzzy Ensemble Design through Multi-Objective Fuzzy Rule Selection.- Applications of Multi-Objective Machine Learning.- Multi-Objective Optimisation for Receiver Operating Characteristic Analysis.- Multi-Objective Design of Neuro-Fuzzy Controllers for Robot Behavior Coordination.- Fuzzy Tuning for the Docking Maneuver Controller of an Automated Guided Vehicle.- A Multi-Objective Genetic Algorithm for Learning Linguistic Persistent Queries in Text Retrieval Environments.- Multi-Objective Neural Network Optimization for Visual Object Detection.
• (source: Nielsen Book Data)

Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.
(source: Nielsen Book Data)

The two volume set LNAI 3613 and LNAI 3614 constitutes the refereed proceedings of the Second International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005, held in Changsha, China, in August 2005 as a joint event in federation with the First International Conference on Natural Computation ICNC 2005 (LNCS volumes 3610, 3611, and 3612). The program committee selected 206 carefully revised full papers and 127 short papers for presentation in two volumes from 1249 submissions. The first volume includes all the contributions related to fuzzy theory and models, uncertainty management and probabilistic methods in data mining, approximate reasoning, axiomatic foundation, fuzzy classifiers, fuzzy clustering, fuzzy database mining and information retrieval, information fusion, neuro-fuzzy systems, fuzzy control, fuzzy hardware, knowledge visualization and exploration, sequential data analysis, parallel and distributed data mining, and rough sets. The second volume deals with dimensionality reduction, pattern recognition and trend analysis, other topics in FSKD methods, mining of spatial, textual, image and time-series data, fuzzy systems in bioinformatics and bio-medical engineering, in expert system and informatics, and in pattern recognition and diagostics; knowledge discovery in bioinformatics, bio-medical engineering, and in expert system and informatics; active information gathering on the web, and neural and fuzzy computation in cognitive computer vision.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Abstracts of Keynote and Invited Talks
• Tactile Situations: A Basis for Manual Intelligence and Learning
• Brain-like Perception and Cognition: Challenges and Thinking
• Dealing with Concept Drifts in Data Streams
• A Novel Bionic Imaging and Its Intelligent Processing
• Skill Learning in Dynamic Scene for Robot Operations
• Emerging Artificial Intelligence Technologies in Healthcare
• Memory Cognition
• Contents
• Brain Cognition
• Mouse-Brain Topology Improved Evolutionary Neural Network for Efficient Reinforcement Learning
• 1 Introduction

• 2 Related Works
• 3 Methods
• 3.1 The Allen Mouse Brain Atlas
• 3.2 The Clustered Hierarchical Circuits
• 3.3 The Neuron Model
• 3.4 Coping the Biological Circuits to Artificial Ones
• 3.5 The Network Learning
• 4 Experiments
• 4.1 The Clustered Brain Regions
• 4.2 The Network Topology from Biological Mouse Brain
• 4.3 Results with Circuit-46 and Random Networks
• 4.4 Result Comparison with Different Algorithms
• 5 Discussion
• References
• DNM-SNN: Spiking Neural Network Based on Dual Network Model
• 1 Introduction
• 2 Methods

• 2.1 Traditional SNN Supervised Learning Algorithm Framework and Its Limitations
• 2.2 Proposed Dual-Model Spike Network Supervised Learning Algorithm
• 2.3 Proposed Multi-channel Mix Module Prediction Method
• 2.4 The Chosen Network Model
• 2.5 Selection of Spiking Neurons
• 3 Experimental Results
• 3.1 Single- and Dual-Model Resnet11 Performance on the CIFAR-10 Dataset
• 3.2 Related Work Comparison
• 4 Conclusion
• References
• A Memetic Algorithm Based on Adaptive Simulated Annealing for Community Detection
• 1 Introduction
• 2 Background
• 2.1 Modularity

• 2.2 Normalized Mutual Information
• 3 Description of MA-ASA
• 3.1 Segmented Label Propagation
• 3.2 Selection and Crossover Operation
• 3.3 Mutation Operation
• 3.4 Improved Simulated Annealing
• 3.5 Framework of MA-ASA
• 4 Experiments and Analysis
• 4.1 Experimental Settings
• 4.2 Experimental Results and Analysis
• 5 Conclusion
• References
• The Model of an Explanation of Self and Self-awareness Based on Need Evolution
• 1 Background and Significance
• 2 The Nature and Needs of Life
• 2.1 The Nature and Representation of the Self
• 2.2 The Primary Needs and Principle of Life

• 3 Evolution and Representation of the Needs of Life
• 3.1 Needs Representation and Original Self-evolution in Single-Celled and Complex Organisms
• 3.2 Representation Needs and Self-awareness of Human
• 4 Self-model Based on the Evolution of Needs
• 4.1 Iterative Model of Needs Evolution
• 4.2 Evolutionary Model of the Self
• 5 Dicussion and Conclusion
• References
• Spiking Neuron Network Based on VTEAM Memristor and MOSFET-LIF Neuron
• 1 Introduction
• 2 Proposed Method
• 2.1 Leaky Integrate-and-Fire Model
• 2.2 Design of LIF Circuit
• 2.3 Correspondence Between Network and Circuit

This book constitutes the refereed proceedings of the 5th International Conference on Intelligence Science, ICIS 2022, held in Xi'an, China, in August 2022. The 41 full and 5 short papers presented in this book were carefully reviewed and selected from 85 submissions. They were organized in topical sections as follows: Brain cognition; machine learning; data intelligence; language cognition; remote sensing images; perceptual intelligence; wireless sensor; and medical artificial intelligence.
(source: Nielsen Book Data)

• Optimum Tracking in Dynamic Environments.- Explicit Memory Schemes for Evolutionary Algorithms in Dynamic Environments.- Particle Swarm Optimization in Dynamic Environments.- Evolution Strategies in Dynamic Environments.- Orthogonal Dynamic Hill Climbing Algorithm: ODHC.- Genetic Algorithms with Self-Organizing Behaviour in Dynamic Environments.- Learning and Anticipation in Online Dynamic Optimization.- Evolutionary Online Data Mining: An Investigation in a Dynamic Environment.- Adaptive Business Intelligence: Three Case Studies.- Evolutionary Algorithms for Combinatorial Problems in the Uncertain Environment of the Wireless Sensor Networks.- Approximation of Fitness Functions.- Individual-based Management of Meta-models for Evolutionary Optimization with Application to Three-Dimensional Blade Optimization.- Evolutionary Shape Optimization Using Gaussian Processes.- A Study of Techniques to Improve the Efficiency of a Multi-Objective Particle Swarm Optimizer.- An Evolutionary Multi-objective Adaptive Meta-modeling Procedure Using Artificial Neural Networks.- Surrogate Model-Based Optimization Framework: A Case Study in Aerospace Design.- Handling Noisy Fitness Functions.- Hierarchical Evolutionary Algorithms and Noise Compensation via Adaptation.- Evolving Multi Rover Systems in Dynamic and Noisy Environments.- A Memetic Algorithm Using a Trust-Region Derivative-Free Optimization with Quadratic Modelling for Optimization of Expensive and Noisy Black-box Functions.- Genetic Algorithm to Optimize Fitness Function with Sampling Error and its Application to Financial Optimization Problem.- Search for Robust Solutions.- Single/Multi-objective Inverse Robust Evolutionary Design Methodology in the Presence of Uncertainty.- Evolving the Tradeoffs between Pareto-Optimality and Robustness in Multi-Objective Evolutionary Algorithms.- Evolutionary Robust Design of Analog Filters Using Genetic Programming.- Robust Salting Route Optimization Using Evolutionary Algorithms.- An Evolutionary Approach For Robust Layout Synthesis of MEMS.- A Hybrid Approach Based on Evolutionary Strategies and Interval Arithmetic to Perform Robust Designs.- An Evolutionary Approach for Assessing the Degree of Robustness of Solutions to Multi-Objective Models.- Deterministic Robust Optimal Design Based on Standard Crowding Genetic Algorithm.
• (source: Nielsen Book Data)

This book compiles recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The book is motivated by the fact that some degree of uncertainty is inevitable in characterizing any realistic engineering systems. Discussion includes representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums.
(source: Nielsen Book Data)

• Swarm and multi-robotic systems.- Human-robot interaction.- Robotic learning and imitation.- Robot navigation, planning and safety.- Humanoid and bio-inspired robots.- Mobile robots and vehicles.- Robot testing and design.- Detection and recognition.- Learning and adaptive behaviours.- Interaction.- Soft and reconfigurable robots.- Service and industrial robots.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 18th Annual Conference on Towards Autonomous Robotics, TAROS 2017, held in Guildford, UK, in July 2017. The 43 revised full papers presented together with 13 short papers were carefully reviewed and selected from 66 submissions. The papers discuss robotics research drawn from a wide and diverse range of topics, such as swarm and multi-robotic systems; human-robot interaction; robotic learning and imitation; robot navigation, planning and safety; humanoid and bio-inspired robots; mobile robots and vehicles; robot testing and design; detection and recognition; learning and adaptive behaviours; interaction; soft and reconfigurable robots; and service and industrial robots.
(source: Nielsen Book Data)

• Optimum Tracking in Dynamic Environments.- Explicit Memory Schemes for Evolutionary Algorithms in Dynamic Environments.- Particle Swarm Optimization in Dynamic Environments.- Evolution Strategies in Dynamic Environments.- Orthogonal Dynamic Hill Climbing Algorithm: ODHC.- Genetic Algorithms with Self-Organizing Behaviour in Dynamic Environments.- Learning and Anticipation in Online Dynamic Optimization.- Evolutionary Online Data Mining: An Investigation in a Dynamic Environment.- Adaptive Business Intelligence: Three Case Studies.- Evolutionary Algorithms for Combinatorial Problems in the Uncertain Environment of the Wireless Sensor Networks.- Approximation of Fitness Functions.- Individual-based Management of Meta-models for Evolutionary Optimization with Application to Three-Dimensional Blade Optimization.- Evolutionary Shape Optimization Using Gaussian Processes.- A Study of Techniques to Improve the Efficiency of a Multi-Objective Particle Swarm Optimizer.- An Evolutionary Multi-objective Adaptive Meta-modeling Procedure Using Artificial Neural Networks.- Surrogate Model-Based Optimization Framework: A Case Study in Aerospace Design.- Handling Noisy Fitness Functions.- Hierarchical Evolutionary Algorithms and Noise Compensation via Adaptation.- Evolving Multi Rover Systems in Dynamic and Noisy Environments.- A Memetic Algorithm Using a Trust-Region Derivative-Free Optimization with Quadratic Modelling for Optimization of Expensive and Noisy Black-box Functions.- Genetic Algorithm to Optimize Fitness Function with Sampling Error and its Application to Financial Optimization Problem.- Search for Robust Solutions.- Single/Multi-objective Inverse Robust Evolutionary Design Methodology in the Presence of Uncertainty.- Evolving the Tradeoffs between Pareto-Optimality and Robustness in Multi-Objective Evolutionary Algorithms.- Evolutionary Robust Design of Analog Filters Using Genetic Programming.- Robust Salting Route Optimization Using Evolutionary Algorithms.- An Evolutionary Approach For Robust Layout Synthesis of MEMS.- A Hybrid Approach Based on Evolutionary Strategies and Interval Arithmetic to Perform Robust Designs.- An Evolutionary Approach for Assessing the Degree of Robustness of Solutions to Multi-Objective Models.- Deterministic Robust Optimal Design Based on Standard Crowding Genetic Algorithm.
• (source: Nielsen Book Data)

This book compiles recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The book is motivated by the fact that some degree of uncertainty is inevitable in characterizing any realistic engineering systems. Discussion includes representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums.
(source: Nielsen Book Data)

• On the effect of scalarising norm choice in a ParEGO implementation.- Multi-objective big data optimization with Metal and Spark.- An empirical assessment of the properties of inverted generational distance indicators on multi- and many-objective optimization.- Solving the Bi-objective traveling thief problem with multi-objective evolutionary algorithms.- Automatically Configuring multi-objective local search using multi-objective optimization.- The multi-objective shortest path problem is NP-hard, or is it.- Angle-based preference models in multi-objective optimization.- Quantitative performance assessment of multi-objective optimizers: The average runtime attainment function.- A multi-objective strategy to allocate roadside units in a vehicular network with guaranteed levels of service.- An approach for the local exploration of discrete many objective optimization problems.- A note on the detection of outliers in a binary outranking relation.- Classifying meta-modeling methodologies for evolutionary multi-objective optimization: First results.- Weighted stress function method for multi-objective evolutionary algorithm based on decomposition.- Timing the decision support for real-world many-objective problems.- On the influence of altering the action set on PROMETHEE II's relative ranks.- Peek { Shape { Grab: a methodology in three stages for approximating the non-dominated points of multi-objective discrete combinatorial optimization problems with a multi-objective meta-heuristic.- A new reduced-length genetic representation for evolutionary multi-objective clustering.- A fast incremental BSP tree archive for non-dominated points.- Adaptive operator selection for many-objective optimization with NSGA-III.- On using decision maker preferences with ParEGO.- First investigations on noisy model-based multi-objective optimization.- Fusion of many-objective non-dominated solutions using reference points.- An expedition to multi-modal multi-objective optimization landscapes.- Neutral neighbors in Bi-objective optimization: Distribution of the most promising for permutation problems.- Multi-objective adaptation of a parameterized GVGAI agent towards several games.- Towards standardized and seamless integration of expert knowledge into multi-objective evolutionary optimization algorithms.- Empirical investigations of reference point based methods when facing a massively large number of objectives: First results.- Building and using an ontology of preference-based multi-objective evolutionary algorithms.- A fitness landscape analysis of pareto local search on Bi-objective permutation flow-shop scheduling problems.- Dimensionality reduction approach for many-objective vehicle routing problem with demand responsive transport.- Heterogeneous evolutionary swarms with partial redundancy solving multi-objective tasks.- Multiple meta-models for robustness estimation in multi-objective robust optimization.- Predator-Prey techniques for solving multi-objective scheduling problems for unrelated parallel machines.- An overview of weighted and unconstrained scalarizing functions.- Multi-objective representation setups for deformation-based design optimization.- Design perspectives of an evolutionary process for multi-objective molecular optimization.- Towards a better balance of diversity and convergence in NSGA-III: First results.- A comparative study of fast adaptive preference-guided evolutionary multi-objective optimization.- A population-based algorithm for learning a majority rule sorting model with coalitional veto.- Injection of extreme points in evolutionary multio-objective optimization algorithms.- The impact of population size, number of children, and number of reference points on the performance of NSGA-III.- Multi-objective optimization for liner shipping fleet repositioning.- Surrogate-assisted partial order-based evolutionary optimization.- Hyper-volume indicator gradient ascent multi-objective optimization.- Toward step-size adaptation in evolutionary multi-objective optimization.- Computing 3-D expected hyper-volume improvement and related integrals in asymptotically optimal time.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017 held in Munster, Germany in March 2017. The 33 revised full papers presented together with 13 poster presentations were carefully reviewed and selected from 72 submissions. The EMO 2017 aims to discuss all aspects of EMO development and deployment, including theoretical foundations; constraint handling techniques; preference handling techniques; handling of continuous, combinatorial or mixed-integer problems; local search techniques; hybrid approaches; stopping criteria; parallel EMO models; performance evaluation; test functions and benchmark problems; algorithm selection approaches; many-objective optimization; large scale optimization; real-world applications; EMO algorithm implementations.
(source: Nielsen Book Data)