Our presentation last week was PCI Chapter 5, Optimization. We as a group broke the chapter down and each create slides based on our assigned sections. Once completed we got together and assembled the slides. There were some interesting things that we learned.
One of the most important pieces to an optimization problem is the cost function. The cost function is also the most difficult things to determine. An optimization problem tries to minimize the cost function.
There are a few methods of optimization. The first is Random Searching. Random Searching is just what the name implies. It is just random guessing. It is only really good for using as a baseline against other algorithms.
Hill Climbing is a variation of Random Searching. It finds its closest neighbors and finds the best value. It is only able to find the local minimum rather than the global one.
Simulated Annealing uses random searching to find an initial value. The algorithm looks for progressively better values. It is much more efficient when it comes to finding the global minimum.
Genetic Algorithms start with a set of random solutions. It takes the best solutions and the rest are considered modifications of the best ones. It continues over an over until no improvement is shown.
Time must also be spent deciding on how to represent the solution. Depending on the type of problem, the solution may vary on how it is represented.
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