Optimization Techniques for Solving Complex Problems

FOREWORD

The topic of optimization, especially in the context of solving complex problems, is of utmost importance to most practitioners who deal with a variety of optimization tasks in real-world settings. These practitioners need a set of new tools for extending existing algorithms and developing new algorithms to address a variety of real-world problems. This book addresses these very issues.

The first part of the book covers many new ideas, algorithms, and techniques. These include modern heuristic methods such as genetic programming, neural networks, genetic algorithms, and hybrid evolutionary algorithms, as well as classic methods such as divide and conquer, branch and bound, dynamic programming, and cryptographic algorithms. Many of these are extended by new paradigms (e.g., new metaheuristics for multiobjective optimization, dynamic optimization) and they address many important and practical issues (e.g., constrained optimization, optimization of time series).

The second part of the book concentrates on various applications and indicates the applicability of these new tools for solving complex real-world problems. These applications include DNA sequencing and reconstruction, location of antennas in telecommunication networks, job scheduling, cutting and packing problems, multidimensional knapsack problems, and image processing, to name a few.

The third and final part of ...