Abstract

The goal of automatic programming is to create, in an automated way, a computer program that enables a computer to solve a problem. Ideally, an automatic programming system should require that the user pre-specify as little as possible about the problem. In particular, it is desirable that the user not be required to specify the size and shape (i.e., the architecture) of the ultimate solution to the problem before applying the technique. This paper describes how the biological theory of gene duplication described in Susumu Ohno's provocative book, Evolution by Means of Gene Duplication, was brought to bear on a vexatious problem from the domain of automated machine learning in the computer science field. The resulting biologically-motivated approach using six new architecture-altering operations enables genetic programming to automatically discover the size and shape of the solution at the same time as it is evolving a solution to the problem. Genetic programming with the architecture-altering operations was used to evolve a computer program to classify a given protein segment as being a transmembrane domain or non-transmembrane area of the protein (without biochemical knowledge, such as

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