PPT Slide
What to look for in an Optimization Technique
continuous: Gradient search, linear programming
discrete: Integer programming, gradient estimators
Ok for search spaces with a single peak/trough
Random search, brute force (exhaustive search)
Ok for small search spaces
Hard problems (large search spaces, multiple peaks/troughs)
need both convergent and divergent behaviors
Genetic algorithms, simulated annealing, learning hill climbers, etc.
These techniques can exploit the peaks/troughs, as well as
intelligently explore the search space.