CSCE 315 Lecture 18
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Optimizing Search Functions
Iterative Improvement
Guess at first, then adjust for more accurate results
Hill Climbing
Start at a random point. Choose the best of 3 random points. Repeat until no improvements
Gradient Ascent
Take gradient of function, then ascend a step in that direction.
Simulated Annealing
"heat up" the entropy of random points. As it cools down, settle on maximum points. Temperature allows accepting of worse states at first
Genetic Algorithms
State = "chromosome" and optimization function = "fitness". Create "generations" of solutions, "kill off" the worst solutions, and "cross-breed" the best to get better state