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Ceci n'est pas un titre: Baby Steps into Genetic Programming

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While my final ranking in the Google AI Contest was disappointing (280th), it was a very educational experience and was totally offset by Gábor Melis' dominating win using Common Lisp as well. One of things that piqued my interest during the contest was a post on the AI Challenge forums about a bot written using genetic programming. Genetic programming (GP) and genetic algorithms have always held my interest but seeing the bot in action really motivated me to dive into the matter. Genetic programming is inspired by biological evolution and is a way of solving problems by setting up an environment (tuned to the problem at hand!) and allowing computer programs to evolve towards a possible solution in that environment. This article shows my initial exploration into GP using Common Lisp and should be an example of a typical REPL session (my session was a couple of hours divided over two evenings). The code has been reviewed, made a more readable and lispier but still looks very much like w

Ceci n'est pas un titre: Baby Steps into Genetic Programming Ceci n'est pas un titre irregular rants and mind-farts 2011-01-18 Baby Steps into Genetic Programming Table of Contents 1 Introduction 1.1 Toy Project 1.2 Environment 2 Generating Random Code 2.1 Operators 2.2 RANDOM-ELT 2.3 Generating Random Function Forms 2.3.1 Limiting RANDOM-FORM 2.4 =INPUT= 2.5 Testing RANDOM-ELT and RANDOM-FORM 3 Running Generated Code 3.1 SBCL note 4 Population 5 Fitness 6 Generation Functions 6.1 Traversing Nodes 6.1.1 N-NODES 6.1.2 Picking Random Nodes 6.1.3 Replacing Nodes 6.2 Cross-overs 6.3 Mutation 7 Adv

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