The Computational Beauty of Nature: Computer Explorations of Fractals, Chaos, Complex Systems, and Adaptation
While reading The Computational Beauty of Nature: Computer Explorations of Fractals, Chaos, Complex Systems, and Adaptation by Gary William Flake, I was most interested in the chapters covering Fractals, Complex Systems (cellular automata, ...) and Adaptation (genetic algorithms, neural networks, ...). I've never been that interested in Chaos, so I skimmed over that part. I would've like to have seen more then just a mere mention of my favorite topic, Genetic Programming. But the book has so much info there probably wasn't enough room for it!I had never been that interested in Fractals. But the book made it clear to me as to why they are more then just some way to create digital art. For some reason I had never made the connection before between Fractal's and L-Systems. But now it all makes sense. The connection is that both areas cover building large structures from a small set of repeating instructions. For some reason I never got that point when reading about Fractal's before. Such sciences could be applied to anything from architecture to computer programming to biology.
Under Complex Systems I found a healthy review of Cellular Automata, Autonomous Agents, Self-Organization, Competition and Cooperation. Just as I found something new regarding L-Systems, I found something new referring to flocking behavior. Years ago I traded a few e-mails with Craig Reynolds. I was trying to decipher the mechanics of his Boids simulation (a simulation of a flock of birds). This book once again provided enough info so that I could probably put together my own simulation. If you'd like to look into it yourself, or just see a simulation, see this link:
http://www.red3d.com/cwr/boids/
Genetic Algorithms and especially Genetic Programming are two of my favorite topics of Computer Science. I did learn a few new things from reading the related chapter on Classifiers. The chapter on Neural Networks was familiar territory. If you are new to the subject, it covers all the basics. Personally, I think all NN's should be trained through GA. But still learning about BackProp can be useful.
Over all, a good solid book. If you are new to the science of Complexity, don't try to read the whole thing straight through. Skip around, like I did, and find the areas that interest you. If your Matrix Math or Calculus is a little rusty, just jump over the formulas. You'll still find a lot of interesting science to play with.
Code generation is too complicated to explain in a blog posting. So I may write an eBook on the subject as it relates to testing at some point. Until then a great place to start is the book
This is my favorite book on the science of Complexity. You won't find any how-to's. This book is the story of several pioneers of the science and the