Evolving a Computer Program to Generate Random Numbers Using the Genetic Programming Paradigm.
John R. Koza · 1991
This paper demonstrates that it is possible to genetically breed a computer program that is considered difficult to write, namely, a randomizer that converts a sequence of consecutive integers into pseudo-random bits with near maximal entropy. 1. INTRODUCTION AND OVERVIEW "How can computers learn to solve problems without being explicitly programmed?" This question, which is a central question in the fields of artificial intelligence and machine learning, can be approached using an analogy to the evolutionary process in nature. John Holland's pioneering 1975 Adaptation in Natural and Artificial Systems [3] described how the evolutionary process in nature can be applied to artificial systems using the "genetic algorithm" operating on fixed length character strings. Representation is a key issue in genetic algorithm work because genetic algorithms directly manipulate the coded representation of the problem and because the representation scheme can severely limit the window by which the...