1. Genetic Algorithms and Machine Learning Brent Harrison

2. Genetic Algorithms Overview Use the concept of natural selection to optimize data. Initial population might not be so good…but that changes rather quickly.

3. Genetic Algorithm Application Mostly used for determining optimal parameters. An example, optimizing sigma values in neural nets (more on that later). A more fun one…optimizing theme park tours.

4. Traveling Salesman Problem A salesman must visit all cities and return to his starting location in the fastest time. Could try brute forcing…but seeing as there are n! permutations, this solution becomes impractical rather quickly.

5. Possible Answer! Hit it with a GA! Modified GA’s will produce an optimal solution most of the time for problems with up to 100,000 cities.

6. Machine Learning Overview They’re algorithms that enable machines to learn…we’ve been over this.

7. Types of Learning Structures Neural Nets: –General Regression Neural Networks –Radial Basis Function Networks –Feed Forward Neural Networks Naive Bayesian Classifiers

8. Machine Learning Applications Data Mining Breast Cancer Diagnosis Show how bad the BCS really is.

9. How Bad is the BCS? By using neural networks, it is possible to simulate the way that poll voters will vote. The predictions are based on past data freely available to anyone.

10. How Bad is the BCS? Using these simulations, we can hit the neural networks with a GA. By doing that, it is possible to evolve the worst BCS season possible. The faster we create this system…the worse the BCS is. Typically...within 5-10 generations we get a bad year.