Transcript Genetic Algorithms and Machine Learning

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.