The first book to comprehensively study both approximation algorithms and
metaheuristics, this resource presents the methodologies to design and analyze
efficient approximation algorithms for a large class of problems, and to
establish inapproximability results for another class of problems. It also
discusses local search, neural networks, and metaheuristics, as well as
multiobjective problems, sensitivity analysis, and stability. After laying this
foundation, the handbook applies the methodologies to classical problems in
combinatorial optimization, computational geometry, and graph problems. In
addition, it explores large-scale and emerging applications in networks,
bioinformatics, VLSI, game theory, and data analysis.
Delineating the tremendous growth in this area, the "Handbook of Approximation
Algorithms and Metaheuristics" covers fundamental,