Hierarchical Genetic Algorithms Operating on Populations of Computer Programs

Existing approaches to artificial intelligence problems such as sequence induction, automatic programming, machine learning, planning, and pattern recognition typically require specification in advance of the size and shape of the solution to the problem (often in a unnatural and difficult way). This paper reports on a new approach in which the size and shape of the solution to such problems is dynamically created using Darwinian principles of reproduction and survival of the fittest. Moreover, the resulting solution is inherently hierarchical. The paper describes computer experiments, using the author's 4341 line LISP program, in five areas of artifical intelligence, namely (1) sequence induction (e.g. inducing a computational procedure for the recursive Fibonacci sequence and inducing a computational procedure for a cubic polynomial sequence), (2) automatic programming (e.g. discovering a computational procedure for solving pairs of linear equations, solving quadratic equations for complex roots, and discovering trigonometric identities), (3) machine learning of functions (e.g. learning a Boolean multiplexer function previously studied in neural net and classifier system work and learning the exclusive-or and parity function), (4) planning (e.g. developing a robotic action sequence that can stack an arbitrary initial configuration of blocks into a specified order), and (5) pattern recognition (e.g. translation-invariant recognition of a simple one dimensional shape in a linear retina).