Introduction

Abstract

This dissertation describes, demonstrates, and analyzes a new search technique called stochastic iterated genetic hillclimbing (SIGH). SIGH performs function optimizations in high-dimensional, binary vector spaces. Although the technique can be characterized in abstract computational terms, it emerged from research into massively parallel connectionist learning machines, and it has a compact implementation in a connectionist architecture. The behavior generated by the machine displays elements of two existing search techniques—stochastic hillclimbing and genetic search.