Yaochu Jin

Yang Chen

Hangyu Zhu

Jinjin Xu

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.

Fundamentals and Advances

Federated Learning

Hisao Ishibuchi

Ke Tang

Peter Whigham

Kay Chen Tan

Yuhui Shi

Xiaodong Li

Mengjie Zhang

Lam Thu Bui

Will N. Browne

Pramod Singh

Grant Dick

This volume constitutes the proceedings of the 10th International Conference on Simulated Evolution and Learning, SEAL 2012, held in Dunedin, New Zealand, in December 2014. The 42 full papers and 29 short papers presented were carefully reviewed and selected from 109 submissions. The papers are organized in topical sections on evolutionary optimization; evolutionary multi-objective optimization; evolutionary machine learning; theoretical developments; evolutionary feature reduction; evolutionary scheduling and combinatorial optimization; real world applications and evolutionary image analysis.

10th International Conference, SEAL 2014, Dunedin, New Zealand, December 15-18, Proceedings

Simulated Evolution and Learning

Yunqiang Yin

Dujuan Wang

This book provides an introduction to the models, methods, and results of some rescheduling problems in the presence of unexpected disruption events, including job unavailability, arrival of new jobs, and machine breakdown. The occurrence of these unexpected disruptions may cause a change in the planned schedule, which may render the originally feasible schedule infeasible. Rescheduling, which involves adjusting the original schedule to account for a disruption, is necessary in order to minimize the effects of the disruption on the performance of the system. This involves a trade-off between finding a cost-effective new schedule and avoiding excessive changes to the original schedule. This book views scheduling theory as practical theory, and it has made sure to emphasize the practical aspects of its topic coverage. Thus, this book considers some scenarios existing in most real-world environments, such as preventive machine maintenance, and deteriorating effect where the actual processing time of a job gets longer along with machine’s usage and age. To alleviate the effect of disruption events, some flexible strategies are adopted, including allocation extra resources to reduce job processing times or rejection the production of some jobs. For each considered scenario, depending on the model settings and on the disruption events, this book addresses the complexity, and the design of efficient exact or approximated algorithms. Especially when optimization methods and analytic tools fall short, this book stresses metaheuristics including improved elitist non-dominated sorting genetic algorithm and differential evolution algorithm. This book also provides extensive numerical studies to evaluate the performance of the proposed algorithms. The problem of rescheduling in the presence of unexpected disruption events is of great importance for the successful implementation of real-world scheduling systems. There is now an astounding body of knowledge in this field. This book is the first monograph on rescheduling. It aims at introducing the author's research achievements in rescheduling. It is written for researchers and Ph.D. students working in scheduling theory and other members of scientific community who are interested in recent scheduling models. Our goal is to enable the reader to know about some new achievements on this topic.

Models and Algorithms

Rescheduling Under Disruptions in Manufacturing Systems

Yang Gao

Constantina Lekakou

Saber Fallah

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.

18th Annual Conference, TAROS 2017, Guildford, UK, July 19–21, 2017, Proceedings

Towards Autonomous Robotic Systems

Fuzzy rule systems have found a wide range of applications in many fields of science and technology. Traditionally, fuzzy rules are generated from human expert knowledge or human heuristics for relatively simple systems. In the last few years, data-driven fuzzy rule generation has been very active. Compared to heuristic fuzzy rules, fuzzy rules generated from data are able to extract more profound knowledge for more complex systems. This book presents a number of approaches to the generation of fuzzy rules from data, ranging from the direct fuzzy inference based to neural net works and evolutionary algorithms based fuzzy rule generation. Besides the approximation accuracy, special attention has been paid to the interpretabil ity of the extracted fuzzy rules. In other words, the fuzzy rules generated from data are supposed to be as comprehensible to human beings as those generated from human heuristics. To this end, many aspects of interpretabil ity of fuzzy systems have been discussed, which must be taken into account in the data-driven fuzzy rule generation. In this way, fuzzy rules generated from data are intelligible to human users and therefore, knowledge about unknown systems can be extracted.

Advanced Fuzzy Systems Design and Applications

Ying Tan

Qingfu Zhang

Martin Middendorf

This book constitutes the refereed proceedings of the 11th International Conference on Simulated Evolution and Learning, SEAL 2017, held in Shenzhen, China, in November 2017. The 85 papers presented in this volume were carefully reviewed and selected from 145 submissions. They were organized in topical sections named: evolutionary optimisation; evolutionary multiobjective optimisation; evolutionary machine learning; theoretical developments; feature selection and dimensionality reduction; dynamic and uncertain environments; real-world applications; adaptive systems; and swarm intelligence.

11th International Conference, SEAL 2017, Shenzhen, China, November 10–13, 2017, Proceedings

Oliver Schütze

Kathrin Klamroth

Heike Trautmann

Günter Rudolph

Christian Grimme

Margaret Wiecek

This book constitutes the refereed proceedings of the 9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017 held in Münster, 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.

9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings

Evolutionary Multi-Criterion Optimization

Lipo Wang

This book and its sister volume, LNAI 3613 and 3614, constitute the proce- ings of the Second International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2005), jointly held with the First International Conference on Natural Computation (ICNC 2005, LNCS 3610, 3611, and 3612) from - gust 27–29, 2005 in Changsha, Hunan, China. FSKD 2005 successfully attracted 1249 submissions from 32 countries/regions (the joint ICNC-FSKD 2005 received 3136 submissions). After rigorous reviews, 333 high-quality papers, i. e. , 206 long papers and 127 short papers, were included in the FSKD 2005 proceedings, r- resenting an acceptance rate of 26. 7%. The ICNC-FSKD 2005 conference featured the most up-to-date research - sults in computational algorithms inspired from nature, including biological, e- logical, and physical systems. It is an exciting and emerging interdisciplinary area in which a wide range of techniques and methods are being studied for dealing with large, complex, and dynamic problems. The joint conferences also promoted cross-fertilization over these exciting and yet closely-related areas, which had a signi?cant impact on the advancement of these important technologies. Speci?c areas included computation with words, fuzzy computation, granular com- tation, neural computation, quantum computation, evolutionary computation, DNA computation, chemical computation, information processing in cells and tissues, molecular computation, arti?cial life, swarm intelligence, ants colony, arti?cial immune systems, etc. , with innovative applications to knowledge d- covery, ?nance, operations research, and more.

Second International Conference, FSKD 2005, Changsha, China, August 27-29, 2005, Proceedings, Part II

Fuzzy Systems and Knowledge Discovery

Second International Conference, FSKD 2005, Changsha, China, August 27-29, 2005, Proceedings

Second International Conference, FSKD 2005, Changsha, China, August 27-29, 2005, Proceedings, Part I

Handing Wang

Chaoli Sun

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.

Integrating Evolutionary Computation, Machine Learning and Data Science

Data-Driven Evolutionary Optimization

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.

Fuzzy Systems in Bioinformatics and Computational Biology

Jürgen Branke

Franz Rothlauf

Penousal Machado

Stefano Cagnoni

David W. Corne

Rolf Drechsler

Elena Marchiori

Juan Romero

George D. Smith

Giovanni Squillero

This book constitutes the refereed joint proceedings of six workshops on evolutionary computing, EvoWorkshops 2005, held in Lausanne, Switzerland in March/April 2005. The 56 revised full papers presented were carefully reviewed and selected from a total of 143 submissions. In accordance with the six workshops covered, the papers are organized in topical sections on evolutionary bioinformatics; evolutionary computing in communications, networks, and connected systems; hardware optimization techniques; evolutionary computation in image analysis and signal processing; evolutionary music and art; and evolutionary algorithms in stochastic and dynamic environments.

Evoworkshops: EvoBIO, EvoCOMNET, EvoHot, EvoIASP, EvoMUSART, and EvoSTOC

Applications of Evolutionary Computing

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.

Multi-Objective Machine Learning

Evolutionary computation (EC) techniques are e?cient nature-inspired pl- ning and optimization methods based on the principles of natural evolution and genetics. Due to their e?ciency and the simple underlying principles, these methods can be used for a large number of problems in the context of problem solving,optimization,andmachinelearning. Alargeandcontinuouslyincreasing number of researchers and practitioners make use of EC techniques in many - plication domains. The book at hand presents a careful selection of relevant EC applications combined with thorough examinations of techniques for a successful application of EC. The presented papers illustrate the current state of the art in the application of EC and should help and inspire researchers and practitioners to develop e?cient EC methods for design and problem solving. All papers in this book were presented during EvoWorkshops 2005, which was a varying collection of workshops on application-oriented aspects of EC. Since 1999, the format of the EvoWorkshops has proved to be very successful and well representative of the advances in the application of EC. Consequently, over the last few years, EvoWorkshops has become one of the major events addressing the application of EC. In contrast to other large conferences in the EC ?eld, the EvoWorkshops focus solely on application aspects of EC and are an important link between EC research and the application of EC in a large variety of di?erent domains.

Shengxiang Yang

Yew-Soon Ong

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.

Evolutionary Computation in Dynamic and Uncertain Environments

Yaochu Jin

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