Recent Advances in Evolutionary Computation for Combinatorial Optimization

This cutting-edge volume presents recent advances in the area of metaheuristic combinatorial optimisation, with a special focus on evolutionary computation methods. Moreover, it addresses local search methods and hybrid approaches.

Recent Advances in Evolutionary Computation for Combinatorial Optimization

This cutting-edge volume presents recent advances in the area of metaheuristic combinatorial optimisation, with a special focus on evolutionary computation methods. Moreover, it addresses local search methods and hybrid approaches.

Recent Advances in Evolutionary Computation for Combinatorial Optimization

Proceedings of the IEEE 68(12), 1497–1514 (1980) Blum, C., Roli, A.: Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison. ACM Computing Surveys 35(3), 268–308 (2003) Glover, F.W., Kochenberger, ...

Recent Advances in Evolutionary Computation for Combinatorial Optimization

Combinatorial optimisation is a ubiquitous discipline whose usefulness spans vast applications domains. The intrinsic complexity of most combinatorial optimisation problems makes classical methods unaffordable in many cases. To acquire practical solutions to these problems requires the use of metaheuristic approaches that trade completeness for pragmatic effectiveness. Such approaches are able to provide optimal or quasi-optimal solutions to a plethora of difficult combinatorial optimisation problems. The application of metaheuristics to combinatorial optimisation is an active field in which new theoretical developments, new algorithmic models, and new application areas are continuously emerging. This volume presents recent advances in the area of metaheuristic combinatorial optimisation, with a special focus on evolutionary computation methods. Moreover, it addresses local search methods and hybrid approaches. In this sense, the book includes cutting-edge theoretical, methodological, algorithmic and applied developments in the field, from respected experts and with a sound perspective.

Evolutionary Computation in Combinatorial Optimization

Briefly, we substitute the metaheuristic engine (viz. genetic algorithm) used in previous versions of the framework for a simulated annealing algorithm. ... Recent Advances in Evolutionary Computation for Combinatorial Optimization.

Evolutionary Computation in Combinatorial Optimization

This book constitutes the refereed proceedings of the 13th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2013, held in Vienna, Austria, in April 2013, colocated with the Evo* 2013 events EuroGP, EvoBIO, EvoMUSART, and EvoApplications. The 23 revised full papers presented were carefully reviewed and selected from 50 submissions. The papers present the latest research and discuss current developments and applications in metaheuristics - a paradigm to effectively solve difficult combinatorial optimization problems appearing in various industrial, economic, and scientific domains. Prominent examples of metaheuristics are ant colony optimization, evolutionary algorithms, greedy randomized adaptive search procedures, iterated local search, simulated annealing, tabu search, and variable neighborhood search. Applications include scheduling, timetabling, network design, transportation and distribution, vehicle routing, the travelling salesman problem, packing and cutting, satisfiability, and general mixed integer programming.

Evolutionary Computation in Combinatorial Optimization

Statistics and Computing (UK) 7(1), 19–34 (1991) 12. Musliu, N.: An iterative heuristic algorithm for tree decomposition. In: Cotta, C., van Hemert, J. (eds.) Recent Advances in Evolutionary Computation for Combinatorial Optimization, ...

Evolutionary Computation in Combinatorial Optimization

This book constitutes the refereed proceedings of the 10th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2010, held in Instanbul, Turkey, in April 2010. The 24 revised full papers presented were carefully reviewed an selected from 69 submissions. The papers present the latest research and discuss current developments and applications in metaheuristics - a paradigm to effectively solve difficult combinatorial optimization problems appearing in various industrial, economical, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, simulated annealing, tabu search, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy radomized adaptive search procedures, estimation of distribution algorithms and ant colony opitmization.

Evolutionary Computation in Combinatorial Optimization

In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation - GECCO 2008, p. 555. ACM Press, New York (2008) 2. ... Recent Advances in the Theory and Application of Fitness Landscapes. ECC, vol. 6, pp. 141–162.

Evolutionary Computation in Combinatorial Optimization

This 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.

Recent Advances in Memetic Algorithms

"Recent Advances in Memetic Algorithms" presents a rich state-of-the-art gallery of works on Memetic algorithms. Recent Advances in Memetic Algorithms is the first book that focuses on this technology as the central topical matter.

Recent Advances in Memetic Algorithms

Memetic algorithms are evolutionary algorithms that apply a local search process to refine solutions to hard problems. Memetic algorithms are the subject of intense scientific research and have been successfully applied to a multitude of real-world problems ranging from the construction of optimal university exam timetables, to the prediction of protein structures and the optimal design of space-craft trajectories. This monograph presents a rich state-of-the-art gallery of works on memetic algorithms. Recent Advances in Memetic Algorithms is the first book that focuses on this technology as the central topical matter. This book gives a coherent, integrated view on both good practice examples and new trends including a concise and self-contained introduction to memetic algorithms. It is a necessary read for postgraduate students and researchers interested in recent advances in search and optimization technologies based on memetic algorithms, but can also be used as complement to undergraduate textbooks on artificial intelligence.

Bioinspired Computation in Combinatorial Optimization

This book will be very valuable for teaching courses on bioinspired computation and combinatorial optimization.

Bioinspired Computation in Combinatorial Optimization

Bioinspired computation methods such as evolutionary algorithms and ant colony optimization are being applied successfully to complex engineering problems and to problems from combinatorial optimization, and with this comes the requirement to more fully understand the computational complexity of these search heuristics. This is the first textbook covering the most important results achieved in this area. The authors study the computational complexity of bioinspired computation and show how runtime behavior can be analyzed in a rigorous way using some of the best-known combinatorial optimization problems -- minimum spanning trees, shortest paths, maximum matching, covering and scheduling problems. A feature of the book is the separate treatment of single- and multiobjective problems, the latter a domain where the development of the underlying theory seems to be lagging practical successes. This book will be very valuable for teaching courses on bioinspired computation and combinatorial optimization. Researchers will also benefit as the presentation of the theory covers the most important developments in the field over the last 10 years. Finally, with a focus on well-studied combinatorial optimization problems rather than toy problems, the book will also be very valuable for practitioners in this field.

Evolutionary Computation in Combinatorial Optimization

“On the evolution of evolutionary algorithms”, in Keijzer, M. (et al.) editors, European Conference on Genetic Programming, pp. 389-398, Springer-Verlag, Berlin, 2004. 16. H. Xiaohui, S. Yuhui, R. Eberhart, “Recent Advances in Particle ...

Evolutionary Computation in Combinatorial Optimization

This book constitutes the refereed proceedings of the 6th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2006, held in Budapest, Hungary in April 2006. The 24 revised full papers presented were carefully reviewed and selected from 77 submissions. The papers include coverage of evolutionary algorithms as well as various other metaheuristics, like scatter search, tabu search, and memetic algorithms.

Evolutionary Computation in Combinatorial Optimization

In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1481–1488. ACM (2018) 9. ... Recent Advances in the Theory and Application of Fitness Landscapes. ECC, vol. 6, pp. 233–262.

Evolutionary Computation in Combinatorial Optimization

This 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.

Differential Evolution A Handbook for Global Permutation Based Combinatorial Optimization

Carlos Cotta and Jano van Hemert (Eds.) Recent Advances in Evolutionary Computation for Combinatorial Optimization,2008 ISBN 978-3-540-70806-3 Vol.154.Oscar Castillo,Patricia Melin,Janusz Kacprzyk and WitoldPedrycz(Eds.) Soft Computing ...

Differential Evolution  A Handbook for Global Permutation Based Combinatorial Optimization

This is the first book devoted entirely to Differential Evolution (DE) for global permutative-based combinatorial optimization. Since its original development, DE has mainly been applied to solving problems characterized by continuous parameters. This means that only a subset of real-world problems could be solved by the original, classical DE algorithm. This book presents in detail the various permutative-based combinatorial DE formulations by their initiators in an easy-to-follow manner, through extensive illustrations and computer code. It is a valuable resource for professionals and students interested in DE in order to have full potentials of DE at their disposal as a proven optimizer. All source programs in C and Mathematica programming languages are downloadable from the website of Springer.

Supply Chain Optimization Design and Management Advances and Intelligent Methods

An evolutionary algorithm with distance measure for the split delivery capacitated arc routing problem. In Recent Advances in Evolutionary Computation for Combinatorial Optimization (Vol. 153, pp. 275–294). Berlin, Heidelberg: Springer.

Supply Chain Optimization  Design  and Management  Advances and Intelligent Methods

Computational Intelligence (CI) is a term corresponding to a new generation of algorithmic methodologies in artificial intelligence, which combines elements of learning, adaptation, evolution and approximate (fuzzy) reasoning to create programs that can be considered intelligent. Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods presents computational intelligence methods for addressing supply chain issues. Emphasis is given to techniques that provide effective solutions to complex supply chain problems and exhibit superior performance to other methods of operations research.

Evolutionary Computation in Combinatorial Optimization

Recent Advances in Intelligent Engineering Systems, vol. 378, pp. 161–191. Springer, Heidelberg (2012). https://doi. org/10.1007/978-3-642-23229-98 Pushak, Y., Hoos, H.: Algorithm configuration landscapes: In: Auger, A., Fonseca, C.M., ...

Evolutionary Computation in Combinatorial Optimization

This 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.

Linkage in Evolutionary Computation

Carlos Cotta and Jano van Hemert (Eds.) Recent Advances in Evolutionary Computation for Combinatorial Optimization, 2008 ISBN 978-3-540-70806-3 Vol.154.Oscar Castillo,Patricia Melin,Janusz Kacprzyk and Witold Pedrycz (Eds.) Soft ...

Linkage in Evolutionary Computation

In recent years, the issue of linkage in GEAs has garnered greater attention and recognition from researchers. Conventional approaches that rely much on ad hoc tweaking of parameters to control the search by balancing the level of exploitation and exploration are grossly inadequate. As shown in the work reported here, such parameters tweaking based approaches have their limits; they can be easily ”fooled” by cases of triviality or peculiarity of the class of problems that the algorithms are designed to handle. Furthermore, these approaches are usually blind to the interactions between the decision variables, thereby disrupting the partial solutions that are being built up along the way.

Evolutionary Computation in Combinatorial Optimization

... decomposition of combinatorial optimization problems. Evolutionary Computation 19(4), 597–637 (2011) 7. Feinsilver, P., Kocik, J.: Krawtchouk polynomials and krawtchouk matrices. In: Recent Advances in Applied Probability, pp.

Evolutionary Computation in Combinatorial Optimization

This book constitutes the refereed proceedings of the 12th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2012, held in Málaga, Spain, in April 2012, colocated with the Evo* 2012 events EuroGP, EvoBIO, EvoMUSART, and EvoApplications. . The 22 revised full papers presented were carefully reviewed and selected from 48 submissions. The papers present the latest research and discuss current developments and applications in metaheuristics - a paradigm to effectively solve difficult combinatorial optimization problems appearing in various industrial, economic, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, simulated annealing, tabu search, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, estimation of distribution algorithms, and ant colony optimization.

Nature Inspired Algorithms for Optimisation

Recent Advances in Evolutionary Computation for Combinatorial Optimization. Studies in Computational Intelligence, ch. 8, pp. 119–136. Springer, Heidelberg (2008) NIST/SEMATECH, e-Handbook of Statistical Methods (2003), ...

Nature Inspired Algorithms for Optimisation

Nature-Inspired Algorithms have been 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.

Evolutionary Computation in Combinatorial Optimization

Recent Advances in Intelligent Engineering Systems. SCI, vol. 378, pp. 161–191. Springer, Heidelberg (2012) Stadler, P.F., Hordijk, W., Fontanari, J.F.: Phase transition and landscape statistics of the number partitioning problem.

Evolutionary Computation in Combinatorial Optimization

This book constitutes the refereed proceedings of the 14th European Conference on Evolutionary Computation in Combinatorial Optimization, Evo COP 2014, held in Granada, Spain, in April 2014, co-located with the Evo*2014 events Euro GP, Evo BIO, Evo MUSART and Evo Applications. The 20 revised full papers presented were carefully reviewed and selected from 42 submissions. The papers cover the following topics: swarm intelligence algorithms, fitness landscapes and adaptive algorithms, real world and routing problems and cooperative and metaheuristic search.

Handbook of Metaheuristics

218(3), 614–623 (2012) N. Nepomuceno, P. Pinheiro, A.L.V. Coelho, A hybrid optimization framework for cutting and packing problems, in Recent Advances in Evolutionary Computation for Combinatorial Optimization, ed. by C. Cotta, ...

Handbook of Metaheuristics

The third edition of this handbook is designed to provide a broad coverage of the concepts, implementations, and applications in metaheuristics. The book’s chapters serve as stand-alone presentations giving both the necessary underpinnings as well as practical guides for implementation. The nature of metaheuristics invites an analyst to modify basic methods in response to problem characteristics, past experiences, and personal preferences, and the chapters in this handbook are designed to facilitate this process as well. This new edition has been fully revised and features new chapters on swarm intelligence and automated design of metaheuristics from flexible algorithm frameworks. The authors who have contributed to this volume represent leading figures from the metaheuristic community and are responsible for pioneering contributions to the fields they write about. Their collective work has significantly enriched the field of optimization in general and combinatorial optimization in particular.Metaheuristics are solution methods that orchestrate an interaction between local improvement procedures and higher level strategies to create a process capable of escaping from local optima and performing a robust search of a solution space. In addition, many new and exciting developments and extensions have been observed in the last few years. Hybrids of metaheuristics with other optimization techniques, like branch-and-bound, mathematical programming or constraint programming are also increasingly popular. On the front of applications, metaheuristics are now used to find high-quality solutions to an ever-growing number of complex, ill-defined real-world problems, in particular combinatorial ones. This handbook should continue to be a great reference for researchers, graduate students, as well as practitioners interested in metaheuristics.

Intelligent Systems and Applications

In: Evolutionary Computation in Combinatorial Optimization, pp. 1–12. Springer, Heidelberg (2007) 3. Alba, E., Luque, G.: A hybrid genetic algorithm for the dna fragment assembly problem. In: Recent Advances in Evolutionary Computation ...

Intelligent Systems and Applications

This book is a remarkable collection of chapters covering a wider range of topics, including unsupervised text mining, anomaly and Intrusion Detection, Self-reconfiguring Robotics, application of Fuzzy Logic to development aid, Design and Optimization, Context-Aware Reasoning, DNA Sequence Assembly and Multilayer Perceptron Networks. The twenty-one chapters present extended results from the SAI Intelligent Systems Conference (IntelliSys) 2015 and have been selected based on high recommendations during IntelliSys 2015 review process. This book presents innovative research and development carried out presently in fields of knowledge representation and reasoning, machine learning, and particularly in intelligent systems in a more broad sense. It provides state - of - the - art intelligent methods and techniques for solving real world problems along with a vision of the future research.

Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management

Recent Advances in Evolutionary Computation for Combinatorial Optimization. Studies in Computational Intelligence, Springer, 153. doi:10.1007/978-3-540-70807-0 Deb, K. (2001). Multi–Objective Optimization Using Evolutionary Algorithms.

Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management

In the digital age, modern society is exposed to high volumes of multimedia information. In efforts to optimize this information, there are new and emerging methods of information retrieval and knowledge management leading to higher efficiency and a deeper understanding of this data. The Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management is a critical scholarly resource that examines bio-inspired classes that solve computer problems. Featuring coverage on a broad range of topics such as big data analytics, bioinformatics, and black hole optimization, this book is geared towards academicians, practitioners, and researchers seeking current research on the use of biomimicry in information and knowledge management.

Advances in Multi Objective Nature Inspired Computing

The purpose of this book is to collect contributions that deal with the use of nature inspired metaheuristics for solving multi-objective combinatorial optimization problems.

Advances in Multi Objective Nature Inspired Computing

The purpose of this book is to collect contributions that deal with the use of nature inspired metaheuristics for solving multi-objective combinatorial optimization problems. Such a collection intends to provide an overview of the state-of-the-art developments in this field, with the aim of motivating more researchers in operations research, engineering, and computer science, to do research in this area. As such, this book is expected to become a valuable reference for those wishing to do research on the use of nature inspired metaheuristics for solving multi-objective combinatorial optimization problems.