A field guide to genetic programming
Genetic programming (GP) is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until high-fitness solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. This unique overview of this exciting technique is written by three of the most active scientists in GP. See www.gp-field-guide.org.uk for more information on the book.
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- "A Field Guide to Genetic Programming" (https://www.amazon.com/Field-Guide-Genetic-Programming/dp/14...) is one of the better books I've read on the matter (but I've yet to read anything by Koza). W. Langdon's webpage (http://www0.cs.ucl.ac.uk/staff/W.Langdon/) has a lot of great information.
- Koza's text is good, but, depending on the student, may be a little deep to start with. I's suggest A Field Guide To Genetic Programming [1] as a better start for most programmers, and then follow up with Koza's books.
[1] http://www.amazon.com/Field-Guide-Genetic-Programming/dp/140...
- It's already documented. I just didn't pay much attention and was thus able to verify what others have observed. I highly recommend the book "A Field Guide to Genetic Programming", which covers this and other associated topics. It's one of the best CS books I've read in the last few years.
- I got a lot of value from A Field Guide to Genetic Programming[1] and Introduction to Evolutionary Computing[2] when I was studying this stuff.
I've heard good things about Essentials of Metaheuristics[3] and Clever Algorithms[4] too, I would like to read them some day.
[1] http://www.gp-field-guide.org.uk/
[2] http://www.cs.vu.nl/~gusz/ecbook/ecbook.html
- > I want a program that will take a set of unit tests, and create a program that takes a piece of code and evolves it until all those tests pass.
Look into Genetic Programming (as opposed to Genetic Algorithms, Evolutionary Programming or Evolutionary strategies, three other fields that arose independently).
In GP, the genotype being evolved is a program, usually represented as either a tree of instructions or a string of assembler instructions. There are other schemes but these are the major two.
However, there's a fly in the ointment. Averaged over every possible computable problem, no one algorithm is the "best". It's called the "no free lunch theorem". The upshot is that with some clever encoding of domain knowledge into the genotypical space, you can often produce systems that outperform human-designed systems. But you cannot start from an absolutely generic genotype and necessarily arrive at the 'best' algorithm, you will always need some degree of manual intervention. Don't ask me to explain it better, I can't because I don't follow the maths.
GP has lots of interesting nooks and crannies, I refer you to A Field Guide to Genetic Programming for a fairly readable introduction: http://www.gp-field-guide.org.uk/
- I found A Field Guide to Genetic Programming to be a good introduction to genetic programming, which is a subset of genetic algorithms. The book can be had freely at http://www.gp-field-guide.org.uk/
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