Answer the following questions for
Monday's reading on Genetic Algorithms (Mitchell GA ch. 1)
In your own words, explain the appeal of applying evolutionary computation to computational problems.
Genetic algorithms are another form of search algorithm. Of all of the search algorithms we saw in the first half of the semester, which one is most similar to GAs and why?
Answer the following question for
Wednesday's reading on Evolving Neural Networks (Mitchell GA sec. 2.3)
One way to evolve neural networks is to pick a fixed network archietecture (the number of input units, hidden units, and output units), and to use a GA to discover a good set of weights.
How are the chromosomes represented in this scenario?
What is a typical fitness function used in this scenario?
For what kinds of tasks would we prefer evolving weights over using backpropagation to learn weights?
No journal response needed for Friday.