“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.)