- author = "John R. Koza",
- title = "Genetic evolution and co-evolution of game strategies",
- booktitle = "The International Conference on Game Theory and Its Applications",
- year = "1992",
- editor = "Sylvain Sorin",
- address = "Stony Brook, New York",
- month = jul # " 15",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://www.genetic-programming.com/jkpdf/icgt1992.pdf",
- size = "14 pages",
- abstract = "The problem of discovering a strategy for playing a game is an important problem in game theory. This problem can be viewed as requiring discovery of a computer program. The desired computer program takes either the entire history of past moves in the game or the current state of the game as its input and produces the next move as its output. This paper describes the recently developed genetic programming paradigm which genetically breeds populations of computer programs to solve problems. In genetic programming, the individuals in the population are independently acting hierarchical compositions of functions and arguments of various sizes and shapes. Each of these individual computer programs is evaluated for its fitness in playing the game. A simulated evolutionary process driven by this measure of fitness then uses the Darwinian principle of reproduction and survival of the fittest and the genetic operation of crossover (sexual recombination) to solve the problem. Genetic programming can also operate simultaneously on two (or more) populations of programs. In such {"}co-evolution,{"} each population acts as the environment for the other population. In particular, each individual of the first population is evaluated for {"}relative fitness{"} by testing it against each individual in the second population, and, simultaneously, each individual in the second population is evaluated for {"}relative fitness{"} by testing it against each individual in the first population. In this paper, genetic programming is illustrated with three different problems. The first problem involves genetically breeding a population of computer programs to find an optimal strategy for a player of a discrete two-person 32-outcome game represented by a game tree in extensive form. In this problem, the entire history of past moves of both players is used as input to the computer program. The second problem involves genetically breeding a minimax control strategy in a differential game with an independently-acting pursuer and evader. In this problem, the state of the game is used as input to the computer program. The third problem illustrates the {"}co-evolution{"} and involves genetically breeding an optimal strategy for a player of a discrete two-person 32-outcome game represented by a game tree in extensive form.",
-
notes = "Paper presented
at
http://dev.gtcenter.org/Archive/ic92.htm",
