Sean Luke has made available an open set of lecture notes on metaheuristics algorithms, Essentials of Metaheuristics. Sean defines a metaheuristic as

“A common but unfortunate name for any stochastic optimization algorithm intended to be the last resort before giving up and using random or brute-force search. Such algorithms are used for problems where you don’t know how to find a good solution, but if shown a candidate solution, you can give it a grade. The algorithmic family includes genetic algorithms, hill-climbing, simulated annealing, ant colony optimization, particle swarm optimization, and so on.”

Such AI algorithms are also often called weak methods, but I like the term metaheuristic better.

The lecture notes look great and the chapters can be used independently for self study or to augment topics in a graduate or undergraduate course. Thanks Sean!

(via Don Miner.)

Related posts:

1. Notes from CASCON 2006
2. Barack Obama on sorting algorithms
3. Notes from the AAAI Sympoium on Computational Approaches to Analyzing Weblogs
4. Free online courses on AI, databases and machine learning
5. Human agent swarm attempts Sudoku solution