Evolution of Hive Intelligence Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Unpublished{widland:1999:ehiGP,

• author = "Tom Widland and Kevin Oishi and Alex Feuchter and Ryan Duryea and Ryan Davies",
• title = "Evolution of Hive Intelligence Using Genetic Programming",
• note = "WWW pages",
• year = "1999",
• email = "Tom Widland ",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.supercomputingchallenge.org/98-99/finalreports/006/",
• broken = "http://www.challenge.nm.org/archive/98-99/finalreports/006/",
• abstract = "Executive Summary: Our project deals with the evolution of hive intelligence using genetic programming with the classic video game Pacman as our model environment. Pacman is an arcade game where a group of {"}ghosts{"} try to catch a Pacman as he attempts to eat all the dots in a maze in order to progress to the next level. Hive intelligence is the concept that a group of individual organisms working together as a cohesive unit can efficiently accomplish a defined task. In our model of Pacman, the ghosts are the individual organisms that are assigned the task of catching Pacman in a maze as quickly as possible. They work together as a team, communicating with each other to catch the Pacmen. At the end of each simulation our program rates them on a fitness scale to determine their prowess as a team. The ghost team that catches the most Pacmen in a specified amount of time gets the highest fitness score. We take the fittest teams and mix their programs (genes) together using a crossover algorithm. We then run another series of simulations and our program tests the fitness of the new generation of ghost teams. Our results show that genetic programming is a powerful means of evolving a routine to be more effective then any human created algorithm. The applications of such a process are staggering. In almost any situation in which computer programs are used to perform a single, definable task in varying situations, genetic programming can be used to increase the efficiency of the program. From simulating the function of organs in the human body to the exploration of planets, genetic programming is a useful tool in creating the best routines for the job.",
• notes = "Code pacman.cpp etc in allfiles.tgz",

}

Genetic Programming entries for Tom Widland Kevin Oishi Alex Feuchter Ryan Duryea Ryan Davies

Citations