New York: American Elsevier

In E. L. Keenan (Ed.), Formal semantics of natural language. Cambridge, England: Cambridge University Press, 329-348.

In D. G. Bobrow and A. Collins (Eds.), Representation and understanding: studies in cognitive science. New York: Academic Press, 185-210. Also In: Ronald Brachman and Hector Levesque, editors, Readings in Knowledge Representation, pages 357-370. Morgan

Also see book version (1980), IJCAI-77. Also Rep. No. TR-347, AI Laboratory, Massachusetts Institute of Technology.

In Schank, 157-288.

IEEE Transwactions on Systems, Man and Cybernetics SMC-5, pp. 409-425

Publication 29, Institute of Linguistics, University of Stockholm.

Fourth International Joint Conference on Artificial Intelligence Advance Papers, pp. 26B-273

 John Holland's pioneering book Adaptation in Natural and Artificial Systems [1975, 2nd ed. 1992] showed how the evolutionary process can be applied to solve a wide variety of problems using a highly parallel technique that is now called the genetic algorithm. The genetic algorithm transforms a population of individual objects, each with an associated fitness value, into a new generation of the population using the Darwinian principle of reproduction and survival of the fittest and naturally occurring genetic operations such as crossover (recombination) and mutation. Each individual in the population represents a possible solution to a given problem. The genetic algorithm attempts to find a very good or best solution to the problem by genetically breeding the population of individuals. In preparing to use the conventional genetic algorithm operating on fixed-length character strings to solve a problem, the user must 1. determine the representation scheme, Ann Arbor: The University of Michigan Press

Tech. rep., Groupe d’Intelligence Artificielle, Universit´e d’Aix-Marseille.