Wednesday, September 05, 2007

Heuristics for NP-hard problems

A while back, Michael was discussing the teaching of heuristics in his undergraduate algorithms class, and had this request:
I wish the standard textbooks would devote a solid chapter on heuristic techniques; in real life, most students are probably more likely to have to implement a heuristic for some NP-hard problem than to have to implement a minimum spanning tree algorithm.
If you remove the word, 'standard', he may just have got his wish. From the press blurb for the Handbook of Approximation Algorithms and Metaheuristics:
Delineating the tremendous growth in this area, the Handbook of Approximation Algorithms and Metaheuristics covers fundamental, theoretical topics as well as advanced, practical applications. It is the first book to comprehensively study both approximation algorithms and metaheuristics. Starting with basic approaches, the handbook 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.
Browsing the table of contents, the reader encounters an entire section on heuristics, with chapters on local search, stochastic local search, neural networks, genetic algorithms, tabu search, simulated annealing, ant colony optimization and the like. The book is published by CRC, which stands for "Organization-That-Singlehandedly-Destroys-Rain-Forests-While-Making-Your-Biceps-Extremely-Strong".

I generally have mixed feelings about CRC books: they are good reference texts, but tend to skim over content way too easily, and CRC has this unhealhy obsession with handbooks for everything. But for teaching heuristics, this might be an invaluable reference.

I should mention that the vast majority of the handbook deals with "proper" approximation algorithms, as well as applications to specific areas. The problem is that approximation algorithms is a fast moving field (though maybe not as fast as in the middle 90s), and so a handbook, being a static snapshot of the time, will get dated quickly.
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