Created by W.Langdon from gp-bibliography.bib Revision:1.4771
Studying games may advance our knowledge both in cognition and artificial intelligence, and, last but not least, games possess a competitive angle that coincides with our human nature, thus motivating researchers.
In this dissertation I explore the application of genetic programming to the development of search heuristics for difficult games. I apply GP to the evolution of solvers for the Rush Hour puzzle and the game of FreeCell, along the way demonstrating a general method for evolving heuristics.
My study produced two Gold and one Bronze HUMIE Awards, and an IEEE Outstanding Paper Award.
Genetic Programming (GP) is a sub-class of evolutionary algorithms, in which a population of solutions to a given problem, embodied as LISP expressions, is improved over time by applying the principles of Darwinian evolution. At each stage, or generation, every solution's quality is measured and assigned a numerical value, called fitness. During the course of evolution, natural (or, in our case, artificial) selection takes place, wherein individuals with high fitness values are more likely to generate offspring.
Following selection, genetic operators are applied to the selected individuals. The most widely used ones are crossover, reproduction, and mutation. The crossover (or recombination) operation is reminiscent of natural gene transfer from parents to offspring.....",
Genetic Programming entries for Achiya Elyasaf