Christian Blum – författare

Here, a team of international experts brings together core ideas for solving complex problems in optimization across a wide variety of real-world settings, including computer science, engineering, transportation, telecommunications, and bioinformatics.

Part One—covers methodologies for complex problem solving including genetic programming, neural networks, genetic algorithms, hybrid evolutionary algorithms, and more.

Part Two—delves into applications including DNA sequencing and reconstruction, location of antennae in telecommunication networks, metaheuristics, FPGAs, problems arising in telecommunication networks, image processing, time series prediction, and more.

All chapters contain examples that illustrate the applications themselves as well as the actual performance of the algorithms.?Optimization Techniques for Solving Complex Problems is a valuable resource for practitioners and researchers who work with optimization in real-world settings.

So-called string problems are abundant in bioinformatics and computational biology. New optimization problems dealing with DNA or protein sequences are constantly arising and researchers are highly in need of efficient optimization techniques for solving them.

One obstacle for optimization practitioners is the atypical nature of these problems which require an interdisciplinary approach in order to solve them efficiently and accurately.

So-called string problems are abundant in bioinformatics and computational biology. New optimization problems dealing with DNA or protein sequences are constantly arising and researchers are highly in need of efficient optimization techniques for solving them.

One obstacle for optimization practitioners is the atypical nature of these problems which require an interdisciplinary approach in order to solve them efficiently and accurately.

This book constitutes the proceedings of the 11th International Conference on Swarm Intelligence, ANTS 2018, held in Rome, Italy, in October 2018.

The 24 full papers and 12 short papers presented in this volume were carefully reviewed and selected from 69 submissions. They are devoted to the field of swarm intelligence as a whole, without any bias towards specific research directions.

This book constitutes the refereed proceedings of the 11th International Workshop on Hybrid Metaheuristics, HM 2019, held in Concepción, Chile, in January 2019.

The 11 revised full papers and 5 short papers presented were carefully reviewed and selected from 23 submissions. The papers present hybridization strategies and explore the integration of new techniques coming from other areas of expertise. They cover a variety of topics such as low-level hybridization, high-level hybridization, portfolio techniques, cooperative search, and theoretical aspects of hybridization.

This book constitutes the proceedings of the 12th International Conference on Swarm Intelligence, ANTS 2020, held online -due to COVID-19- in Barcelona Spain, in October 2020. The 20 full papers presented , together with 8 short papers and 5 extended abstracts were carefully reviewed and selected from 50 submissions.

ANTS 2020 contributions are dealing with any aspect of swarm intelligence. 

This book describes a general hybrid metaheuristic for combinatorial optimization labeled Construct, Merge, Solve & Adapt (CMSA). The general idea of standard CMSA is the following one. At each iteration, a number of valid solutions to the tackled problem instance are generated in a probabilistic way. Hereby, each of these solutions is composed of a set of solution components. The components found in the generated solutions are then added to an initially empty sub-instance. Next, an exact solver is applied in order to compute the best solution of the sub-instance, which is then used to update the sub-instance provided as input for the next iteration. In this way, the power of exact solvers can be exploited for solving problem instances much too large for a standalone application of the solver.

Important research lines on CMSA from recent years are covered in this book. After an introductory chapter about standard CMSA, subsequent chapters cover a self-adaptive CMSA variant as well as a variant equipped with a learning component for improving the quality of the generated solutions over time. Furthermore, on outlining the advantages of using set-covering-based integer linear programming models for sub-instance solving, the author shows how to apply CMSA to problems naturally modelled by non-binary integer linear programming models. The book concludes with a chapter on topics such as the development of a problem-agnostic CMSA and the relation between large neighborhood search and CMSA. Combinatorial optimization problems used in the book as test cases include the minimum dominating set problem, the variable-sized bin packing problem, and an electric vehicle routing problem.

The book will be valuable and is intended for researchers, professionals and graduate students working in a wide range of fields, such as combinatorial optimization, algorithmics, metaheuristics, mathematical modeling, evolutionary computing, operations research, artificial intelligence, or statistics.

Politische Akteure rekurrieren zur Rechtfertigung ihres Handelns wieder verstärkt auf die Idee des allgemeinen Wohls. Kann die politische Philosophie eine Bestimmung des Gemeinwohls anbieten, anhand derer sich gerechtfertigte von ungerechtfertigten Gemeinwohlrekursen unterscheiden lassen? Das Paradigma des Prozeduralismus besagt, dass das Gemeinwohl allein über die Angabe formaler Verfahrenskriterien als Output eines wohlgeordneten demokratischen Systems definiert werden kann. Der Autor weist dieses Paradigma zurück und optiert für eine integrative Gemeinwohltheorie. Diese besteht aus zwei Komponenten. Erstere besagt, dass die Mitglieder eines Gemeinwesens durch demokratische Verfahren, die der Umsetzung ihrer subjektiven Interessen dienen, selbst festlegen können, worin das Gemeinwohl besteht - es sei denn, sie verstoßen damit gegen objektive und prozedurtranszendente Mindeststandards. Die zweite Komponente besteht aus einem Katalog dieser Standards, die festlegen, welche Outputs entgegen dem Für-Gut-Halten der Mitglieder gemeinwohlschädlich oder -irrelevant sind. Somit ergibt sich die Gemeinwohldienlichkeit einer Handlung daraus, dass diese demokratisch autorisiert ist und objektiv-prozedurtranszendente Mindeststandards nicht verletzt.

Politische Akteure rekurrieren zur Rechtfertigung ihres Handelns wieder verstärkt auf die Idee des allgemeinen Wohls. Kann die politische Philosophie eine Bestimmung des Gemeinwohls anbieten, anhand derer sich gerechtfertigte von ungerechtfertigten Gemeinwohlrekursen unterscheiden lassen? Das Paradigma des Prozeduralismus besagt, dass das Gemeinwohl allein über die Angabe formaler Verfahrenskriterien als Output eines wohlgeordneten demokratischen Systems definiert werden kann. Der Autor weist dieses Paradigma zurück und optiert für eine integrative Gemeinwohltheorie. Diese besteht aus zwei Komponenten. Erstere besagt, dass die Mitglieder eines Gemeinwesens durch demokratische Verfahren, die der Umsetzung ihrer subjektiven Interessen dienen, selbst festlegen können, worin das Gemeinwohl besteht - es sei denn, sie verstoßen damit gegen objektive und prozedurtranszendente Mindeststandards. Die zweite Komponente besteht aus einem Katalog dieser Standards, die festlegen, welche Outputs entgegen dem Für-Gut-Halten der Mitglieder gemeinwohlschädlich oder -irrelevant sind. Somit ergibt sich die Gemeinwohldienlichkeit einer Handlung daraus, dass diese demokratisch autorisiert ist und objektiv-prozedurtranszendente Mindeststandards nicht verletzt.

This book explains the most prominent and some promising new, general techniques that combine metaheuristics with other optimization methods. A first introductory chapter reviews the basic principles of local search, prominent metaheuristics, and tree search, dynamic programming, mixed integer linear programming, and constraint programming for combinatorial optimization purposes. The chapters that follow present five generally applicable hybridization strategies, with exemplary case studies on selected problems: incomplete solution representations and decoders; problem instance reduction; large neighborhood search; parallel non-independent construction of solutions within metaheuristics; and hybridization based on complete solution archives.     

The authors are among the leading researchers in the hybridization of metaheuristics with other techniques for optimization, and their work reflects the broad shift to problem-oriented rather than algorithm-oriented approaches, enabling faster and more effective implementation in real-life applications. This hybridization is not restricted to different variants of metaheuristics but includes, for example, the combination of mathematical programming, dynamic programming, or constraint programming with metaheuristics, reflecting cross-fertilization in fields such as optimization, algorithmics, mathematical modeling, operations research, statistics, and simulation. The book is a valuable introduction and reference for researchers and graduate students in these domains.

This book constitutes the refereed proceedings of the 10th International Workshop on Hybrid Metaheuristics, HM 2016, held in Plymouth, UK, in June 2016.

The 15 revised full papers presented were carefully reviewed and selected from 43 submissions. The selected papers are of interest for all the researchers working on integrating metaheuristics with other areas for solving both optimization and constraint satisfaction problems. They represent as well a sample of current research demonstrating how metaheuristics can be integrated with integer linear programming and other operational research techniques for tackling difficult and relevant problems.