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Evolutionary Algorithms for Food Science and Technology
by Evelyne Lutton Alberto Tonda Nathalie PerrotResearchers and practitioners in food science and technology routinely face several challenges, related to sparseness and heterogeneity of data, as well as to the uncertainty in the measurements and the introduction of expert knowledge in the models. Evolutionary algorithms (EAs), stochastic optimization techniques loosely inspired by natural selection, can be effectively used to tackle these issues. In this book, we present a selection of case studies where EAs are adopted in real-world food applications, ranging from model learning to sensitivity analysis.
Evolutionary Algorithms for Mobile Ad Hoc Networks
by Yoann Pigné Grégoire Danoy Pascal Bouvry Bernabé Dorronsoro Patricia RuizDescribes how evolutionary algorithms (EAs) can be used to identify, model, and minimize day-to-day problems that arise for researchers in optimization and mobile networkingMobile ad hoc networks (MANETs), vehicular networks (VANETs), sensor networks (SNs), and hybrid networks--each of these require a designer's keen sense and knowledge of evolutionary algorithms in order to help with the common issues that plague professionals involved in optimization and mobile networking.This book introduces readers to both mobile ad hoc networks and evolutionary algorithms, presenting basic concepts as well as detailed descriptions of each. It demonstrates how metaheuristics and evolutionary algorithms (EAs) can be used to help provide low-cost operations in the optimization process--allowing designers to put some "intelligence" or sophistication into the design. It also offers efficient and accurate information on dissemination algorithms, topology management, and mobility models to address challenges in the field.Evolutionary Algorithms for Mobile Ad Hoc Networks:Instructs on how to identify, model, and optimize solutions to problems that arise in daily researchPresents complete and up-to-date surveys on topics like network and mobility simulatorsProvides sample problems along with solutions/descriptions used to solve each, with performance comparisonsCovers current, relevant issues in mobile networks, like energy use, broadcasting performance, device mobility, and moreEvolutionary Algorithms for Mobile Ad Hoc Networks is an ideal book for researchers and students involved in mobile networks, optimization, advanced search techniques, and multi-objective optimization.
Evolutionary Algorithms, Swarm Dynamics and Complex Networks
by Ivan Zelinka Guanrong ChenEvolutionary algorithms constitute a class of well-known algorithms, which are designed based on the Darwinian theory of evolution and Mendelian theory of heritage. They are partly based on random and partly based on deterministic principles. Due to this nature, it is challenging to predict and control its performance in solving complex nonlinear problems. Recently, the study of evolutionary dynamics is focused not only on the traditional investigations but also on the understanding and analyzing new principles, with the intention of controlling and utilizing their properties and performances toward more effective real-world applications. In this book, based on many years of intensive research of the authors, is proposing novel ideas about advancing evolutionary dynamics towards new phenomena including many new topics, even the dynamics of equivalent social networks. In fact, it includes more advanced complex networks and incorporates them with the CMLs (coupled map lattices), which are usually used for spatiotemporal complex systems simulation and analysis, based on the observation that chaos in CML can be controlled, so does evolution dynamics. All the chapter authors are, to the best of our knowledge, originators of the ideas mentioned above and researchers on evolutionary algorithms and chaotic dynamics as well as complex networks, who will provide benefits to the readers regarding modern scientific research on related subjects.
Evolutionary Approach to Machine Learning and Deep Neural Networks: Neuro-Evolution and Gene Regulatory Networks
by Hitoshi IbaThis book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields.Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution. The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.
Evolutionary Artificial Intelligence: Proceedings of ICEAI 2023 (Algorithms for Intelligent Systems)
by Francisco M. Gonzalez-Longatt Przemyslaw Falkowski-Gilski R. Kanthavel David AsirvathamThis book gathers a collection of selected works and new research results of scholars and graduate students presented at International Conference on Evolutionary Artificial Intelligence (ICEAI 2023) held in Malaysia during 13-14 September 2023. The focus of the book is interdisciplinary in nature and includes research on all aspects of evolutionary computation to find effective solutions to a wide range of computationally difficult problems. The book covers topics such as particle swarm optimization, evolutionary programming, genetic programming, hybrid evolutionary algorithms, ant colony optimization, evolutionary neural networks, evolutionary reinforcement learning, genetic algorithms, memetic algorithms, novel bio-inspired algorithms, evolving multi-agent systems, agent-based evolutionary approaches, and evolutionary game theory.
Evolutionary Bioinformatics
by Donald R. ForsdykeNow in its third edition and supplemented with more online material, this book aims to make the "new" information-based (rather than gene-based) bioinformatics intelligible both to the "bio" people and the "info" people. Books on bioinformatics have traditionally served gene-hunters, and biologists who wish to construct family trees showing tidy lines of descent. While dealing extensively with the exciting topics of gene discovery and database-searching, such books have hardly considered genomes as information channels through which multiple forms and levels of information have passed through the generations. This "new bioinformatics" contrasts with the "old" gene-based bioinformatics that so preoccupies previous texts. Forms of information that we are familiar with (mental, textual) are related to forms with which we are less familiar (hereditary). The book extends a line of evolutionary thought that leads from the nineteenth century (Darwin, Butler, Romanes, Bateson), through the twentieth (Goldschmidt, White), and into the twenty first (the final works of the late Stephen Jay Gould). Long an area of controversy, diverging views may now be reconciled.
Evolutionary Biology
by Pierre PontarottiSince 1997, scientists of different disciplines sharing a deep interest in concepts and knowledge related to evolutionary biology have held the annual Evolutionary Biology Meetings in Marseille in order to discuss their research and promote collaboration. Lately scientists especially focusing on applications have also joined the group. This book starts with the report of the "12th Evolutionary Biology Meeting", which gives a general idea of the meeting's epistemological stance. This is followed by 22 chapters, a selection of the most representative contributions, which are grouped under the following four themes: Part I Concepts and Knowledge - Part II Modelization - Part III Applied Evolutionary Biology - Part IV Applications in Other Fields -Part IV transcends the field of biology, presenting applications of evolutionary biology in economics and astronomy.
Evolutionary Computation (International Series on Computational Intelligence)
by D. Dumitrescu Beatrice Lazzerini Lakhmi C. Jain A. DumitrescuRapid advances in evolutionary computation have opened up a world of applications-a world rapidly growing and evolving. Decision making, neural networks, pattern recognition, complex optimization/search tasks, scheduling, control, automated programming, and cellular automata applications all rely on evolutionary computation. Evolutionary Com
Evolutionary Computation 1: Basic Algorithms and Operators (Ieee Press Series On Computational Intelligence Ser. #1)
by Thomas Baeck D. B Fogel Z MichalewiczThe field of evolutionary computation is expanding dramatically, fueled by the vast investment that reflects the value of applying its techniques. Culling material from the Handbook of Evolutionary Computation, Evolutionary Computation 1: Basic Algorithms and Operators contains up-to-date information on algorithms and operators used in evolutionary computing. This volume discusses the basic ideas that underlie the main paradigms of evolutionary algorithms, evolution strategies, evolutionary programming, and genetic programming. It is intended to be used by individual researchers, teachers, and students working and studying in this expanding field.
Evolutionary Computation Techniques: A Comparative Perspective
by Erik Cuevas Diego Oliva Valentín OsunaThis book compares the performance of various evolutionary computation (EC) techniques when they are faced with complex optimization problems extracted from different engineering domains. Particularly focusing on recently developed algorithms, it is designed so that each chapter can be read independently. Several comparisons among EC techniques have been reported in the literature, however, they all suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. In each chapter, a complex engineering optimization problem is posed, and then a particular EC technique is presented as the best choice, according to its search characteristics. Lastly, a set of experiments is conducted in order to compare its performance to other popular EC methods.
Evolutionary Computation and Complex Networks
by Hussein A. Abbass Jing Liu Kay Chen TanThis book introduces the linkage between evolutionary computation and complex networks and the advantages of cross-fertilising ideas from both fields. Instead of introducing each field individually, the authors focus on the research that sits at the interface of both fields. The book is structured to address two questions: (1) how complex networks are used to analyze and improve the performance of evolutionary computation methods? (2) how evolutionary computation methods are used to solve problems in complex networks? The authors interweave complex networks and evolutionary computing, using evolutionary computation to discover community structure, while also using network analysis techniques to analyze the performance of evolutionary algorithms. The book is suitable for both beginners and senior researchers in the fields of evolutionary computation and complex networks.
Evolutionary Computation in Combinatorial Optimization
by Bin Hu Francisco Chicano Pablo García-SánchezThisbook constitutes the refereed proceedings of the 16th European Conference onEvolutionary Computation in Combinatorial Optimization, EvoCOP 2016, held in Porto,Portugal, in March/April 2016, co-located with the Evo*2015 events EuroGP,EvoMUSART and EvoApplications. The17 revised full papers presented were carefully reviewed and selected from 44submissions. The papers cover methodology, applications and theoretical studies. Themethods included evolutionary and memetic algorithms, variable neighborhoodsearch, particle swarm optimization, hyperheuristics, mat-heuristic and otheradaptive approaches. Applications included both traditional domains, such asgraph coloring, vehicle routing, the longest common subsequence problem, thequadratic assignment problem; and new(er) domains such as the traveling thiefproblem, web service location, and finding short addition chains. Thetheoretical studies involved fitness landscape analysis, local search and recombinationoperator analysis, and the big valley search space hypothesis. Theconsideration of multiple objectives, dynamic and noisy environments was alsopresent in a number of articles.
Evolutionary Computation in Combinatorial Optimization
by Bin Hu Manuel López-IbáñezThis book constitutes the refereed proceedings of the 16th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2016, held in Porto, Portugal, in March/April 2016, co-located with the Evo*2015 events EuroGP, EvoMUSART and EvoApplications. The 17 revised full papers presented were carefully reviewed and selected from 44 submissions. The papers cover methodology, applications and theoretical studies. The methods included evolutionary and memetic algorithms, variable neighborhood search, particle swarm optimization, hyperheuristics, mat-heuristic and other adaptive approaches. Applications included both traditional domains, such as graph coloring, vehicle routing, the longest common subsequence problem, the quadratic assignment problem; and new(er) domains such as the traveling thief problem, web service location, and finding short addition chains. The theoretical studies involved fitness landscape analysis, local search and recombination operator analysis, and the big valley search space hypothesis. The consideration of multiple objectives, dynamic and noisy environments was also present in a number of articles.
Evolutionary Computation in Combinatorial Optimization
by Francisco Chicano Gabriela OchoaThis book constitutes the refereed proceedings of the 15th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2015, held in Copenhagen, Denmark, in April 2015, co-located with the Evo*2015 events EuroGP, EvoMUSART and EvoApplications. The 19 revised full papers presented were carefully reviewed and selected from 46 submissions. The papers cover methodology, applications and theoretical studies. The methods included evolutionary and memetic (hybrid) algorithms, iterated local search, variable neighbourhood search, ant colony optimization, artificial immune systems, hyper-heuristics and other adaptive approaches. The applications include both traditional domains, such as graph coloring, knapsack, vehicle routing, job-shop scheduling, the p-median and the orienteering problems; and new(er) domains such as designing deep recurrent neural networks, detecting network community structure, lock scheduling of ships, cloud resource management, the fire-fighter problem and AI planning. The theoretical studies involved approximation ratio, runtime and black-box complexity analyses.
Evolutionary Computation in Combinatorial Optimization: 18th European Conference, EvoCOP 2018, Parma, Italy, April 4–6, 2018, Proceedings (Lecture Notes in Computer Science #10782)
by Manuel López-Ibáñez Arnaud LiefoogheThis book constitutes the refereed proceedings of the 18th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2018, held in Parma, Italy, in April 2018, co-located with the Evo* 2018 events EuroGP, EvoMUSART and EvoApplications. The 12 revised full papers presented were carefully reviewed and selected from 37 submissions. The papers cover a wide spectrum of topics, ranging from the foundations of evolutionary computation algorithms and other search heuristics, to their accurate design and application to both single- and multi-objective combinatorial optimization problems. Fundamental and methodological aspects deal with runtime analysis, the structural properties of fitness landscapes, the study of metaheuristics core components, the clever design of their search principles, and their careful selection and configuration by means of automatic algorithm configuration and hyper-heuristics. Applications cover conventional academic domains such as NK landscapes, binary quadratic programming, traveling salesman, vehicle routing, or scheduling problems, and also include real-world domains in clustering, commercial districting and winner determination.
Evolutionary Computation in Combinatorial Optimization: 19th European Conference, EvoCOP 2019, Held as Part of EvoStar 2019, Leipzig, Germany, April 24–26, 2019, Proceedings (Lecture Notes in Computer Science #11452)
by Arnaud Liefooghe Luís PaqueteThis book constitutes the refereed proceedings of the 19th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2019, held as part of Evo* 2019, in Leipzig, Germany, in April 2019, co-located with the Evo* 2019 events EuroGP, EvoMUSART and EvoApplications.The 14 revised full papers presented were carefully reviewed and selected from 37 submissions. The papers cover a wide spectrum of topics, ranging from the foundations of evolutionary computation algorithms and other search heuristics to their accurate design and application to both single- and multi-objective combinatorial optimization problems. Fundamental and methodological aspects deal with runtime analysis, the structural properties of fitness landscapes, the study of metaheuristics core components, the clever design of their search principles, and their careful selection and configuration. Applications cover domains such as scheduling, routing, partitioning and general graph problems.
Evolutionary Computation in Combinatorial Optimization: 20th European Conference, EvoCOP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15–17, 2020, Proceedings (Lecture Notes in Computer Science #12102)
by Luís Paquete Christine ZargesThis book constitutes the refereed proceedings of the 20th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2020, held as part of Evo*2020, in Seville, Spain, in April 2020, co-located with the Evo*2020 events EuroGP, EvoMUSART and EvoApplications.The 14 full papers presented in this book were carefully reviewed and selected from 37 submissions. The papers cover a wide spectrum of topics, ranging from the foundations of evolutionary computation algorithms and other search heuristics, to their accurate design and application to combinatorial optimization problems.
Evolutionary Computation in Combinatorial Optimization: 21st European Conference, EvoCOP 2021, Held as Part of EvoStar 2021, Virtual Event, April 7–9, 2021, Proceedings (Lecture Notes in Computer Science #12692)
by Christine Zarges Sébastien VerelThis book constitutes the refereed proceedings of the 21st European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2021, held as part of Evo*2021, as Virtual Event, in April 2021, co-located with the Evo*2021 events: EvoMUSART, EvoApplications, and EuroGP. The 14 revised full papers presented in this book were carefully reviewed and selected from 42 submissions. They cover a wide spectrum of topics, ranging from the foundations of evolutionary algorithms and other search heuristics to their accurate design and application to combinatorial optimization problems. Fundamental and methodological aspects deal with runtime analysis, the structural properties of fitness landscapes, the study of core components of metaheuristics, the clever design of their search principles, and their careful selection and configuration. Applications cover problem domains such as scheduling, routing, search-based software engineering and general graph problems. The range of topics covered in this volume reflects the current state of research in the fields of evolutionary computation and combinatorial optimization.
Evolutionary Computation in Combinatorial Optimization: 22nd European Conference, EvoCOP 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings (Lecture Notes in Computer Science #13222)
by Sébastien Verel Leslie Pérez CáceresThis book constitutes the refereed proceedings of the 22nd European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2022, held as part of Evo*2022, in Madrid, Spain, during April 20-21, 2022, co-located with the Evo*2022 events: EvoMUSART, EvoApplications, and EuroGP.The 13 revised full papers presented in this book were carefully reviewed and selected from 28 submissions. They present recent theoretical and experimental advances in combinatorial optimization, evolutionary algorithms, and related research fields.
Evolutionary Computation in Combinatorial Optimization: 23rd European Conference, EvoCOP 2023, Held as Part of EvoStar 2023, Brno, Czech Republic, April 12–14, 2023, Proceedings (Lecture Notes in Computer Science #13987)
by Thomas Stützle Leslie Pérez CáceresThis book constitutes the refereed proceedings of the 23rd European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2023, held as part of Evo*2023, in Brno, Czech Republic in April 2023, co-located with the Evo*2023 events: EvoMUSART, EvoApplications, and EuroGP.The 15 revised full papers presented in this book were carefully reviewed and selected from 32 submissions. They present recent theoretical and experimental advances in combinatorial optimization, evolutionary algorithms, and related research fields.
Evolutionary Computation in Combinatorial Optimization: 24th European Conference, EvoCOP 2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3–5, 2024, Proceedings (Lecture Notes in Computer Science #14632)
by Thomas Stützle Markus WagnerThis book constitutes the referred proceedings of the 24th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2024, held as part of EvoStar 2024, in Aberystwyth, UK, during April 3–5, 2024. The 12 full papers presented in this book were carefully reviewed and selected from 28 submissions. They cover a variety of topics, ranging from constructive algorithms, machine learning techniques ranging from neural network based guidance to sparse surrogate models for optimization problems, the foundation of evolutionary computation algorithms and other search heuristics, to multi-objective optimization problems.
Evolutionary Computation in Combinatorial Optimization: 25th European Conference, EvoCOP 2025, Held as Part of EvoStar 2025, Trieste, Italy, April 23–25, 2025, Proceedings (Lecture Notes in Computer Science #15610)
by Markus Wagner Martin S. KrejcaThis book constitutes the referred proceedings of the 25th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2025, held as part of EvoStar 2025, in Trieste, Italy, during April 23–25, 2025.The 16 full papers presented in this book were carefully reviewed and selected from 43 submissions. These papers cover a variety of topics, ranging from benchmark creation, over genetic programming, heuristics for real-world and NP-hard problems, as well as the foundations of evolutionary computation algorithms and other search heuristics, to both mixed-binary and multi-objective optimization.
Evolutionary Computation in Gene Regulatory Network Research
by Nasimul Noman Hitoshi IbaIntroducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC). The book is organized into four parts that deliver materials in a way equally attractive for a reader with training in computation or biology. Each of these sections, authored by well-known researchers and experienced practitioners, provides the relevant materials for the interested readers. The first part of this book contains an introductory background to the field. The second part presents the EC approaches for analysis and reconstruction of GRN from gene expression data. The third part of this book covers the contemporary advancements in the automatic construction of gene regulatory and reaction networks and gives direction and guidelines for future research. Finally, the last part of this book focuses on applications of GRNs with EC in other fields, such as design, engineering and robotics. * Provides a reference for current and future research in gene regulatory networks (GRN) using evolutionary computation (EC) * Covers sub-domains of GRN research using EC, such as expression profile analysis, reverse engineering, GRN evolution, applications * Contains useful contents for courses in gene regulatory networks, systems biology, computational biology, and synthetic biology * Delivers state-of-the-art research in genetic algorithms, genetic programming, and swarm intelligence Evolutionary Computation in Gene Regulatory Network Research is a reference for researchers and professionals in computer science, systems biology, and bioinformatics, as well as upper undergraduate, graduate, and postgraduate students. Hitoshi Iba is a Professor in the Department of Information and Communication Engineering, Graduate School of Information Science and Technology, at the University of Tokyo, Toyko, Japan. He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the journal of Genetic Programming and Evolvable Machines. Nasimul Noman is a lecturer in the School of Electrical Engineering and Computer Science at the University of Newcastle, NSW, Australia. From 2002 to 2012 he was a faculty member at the University of Dhaka, Bangladesh. Noman is an Editor of the BioMed Research International journal. His research interests include computational biology, synthetic biology, and bioinformatics.
Evolutionary Computation with Biogeography-based Optimization
by Dan Simon Haiping MaEvolutionary computation algorithms are employed to minimize functions with large number of variables. Biogeography-based optimization (BBO) is an optimization algorithm that is based on the science of biogeography, which researches the migration patterns of species. These migration paradigms provide the main logic behind BBO. Due to the cross-disciplinary nature of the optimization problems, there is a need to develop multiple approaches to tackle them and to study the theoretical reasoning behind their performance. This manuscript intends to explain the mathematical model of BBO algorithm and its variants created to cope with continuous domain problems (with and without constraints) and combinatorial problems. Due to the cross-disciplinary nature of the optimization problems, there is a need to develop multiple approaches to tackle them and to study the theoretical reasoning behind their performance. This manuscript intends to explain the mathematical model of BBO algorithm and its variants created to cope with continuous domain problems (with and without constraints) and combinatorial problems.
Evolutionary Computation: A Unified Approach
by Kenneth A. de JongChoice Outstanding Academic Title, 2006. Evolutionary computation, the use of evolutionary systems as computational processes for solving complex problems, is a tool used by computer scientists and engineers who want to harness the power of evolution to build useful new artifacts, by biologists interested in developing and testing better models of natural evolutionary systems, and by artificial life scientists for designing and implementing new artificial evolutionary worlds. In this clear and comprehensive introduction to the field, Kenneth De Jong presents an integrated view of the state of the art in evolutionary computation. Although other books have described such particular areas of the field as genetic algorithms, genetic programming, evolution strategies, and evolutionary programming, Evolutionary Computation is noteworthy for considering these systems as specific instances of a more general class of evolutionary algorithms. This useful overview of a fragmented field is suitable for classroom use or as a reference for computer scientists and engineers.