Lecture notes on AI metaheuristic algorithms

August 23rd, 2009

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.)