Genetic Algorithms and Machine Learning

Brent Harrison

Genetic Algorithms Overview

• Use the concept of natural selection to optimize data.

• Initial population might not be so good…but that changes rather quickly.

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.

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.

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.

Machine Learning Overview

• They’re algorithms that enable machines to learn…we’ve been over this.

Types of Learning Structures

• Neural Nets:– General Regression Neural Networks– Radial Basis Function Networks– Feed Forward Neural Networks

• Naive Bayesian Classifiers

Machine Learning Applications

• Data Mining

• Breast Cancer Diagnosis

• Show how bad the BCS really is.

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.

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.