Optimization Techniques for Solving Complex Problems

Real-world problems and modern optimization techniques to solve them

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.

ENRIQUE ALBA is a Professor of Data Communications and Evolutionary Algorithms at the University of Málaga, Spain.

CHRISTIAN BLUM is a Research Fellow at the ALBCOM research group of the Universitat Politècnica de Catalunya, Spain.

PEDRO ISASI is a Professor of Artificial Intelligence at the University Carlos III of Madrid, Spain.

COROMOTO LEÓN is a Professor of Language Processors and Distributed Programming at the University of La Laguna, Spain.

JUAN ANTONIO GÓMEZ is a Professor of Computer Architecture and Reconfigurable Computing at the University of Extremadura, Spain.