Retaining Experience and Growing Solutions

TL;DR

This paper introduces the Node-by-Node Growth Solver (NNGS), a novel genetic programming algorithm that retains experience across related problems, significantly improving efficiency and success rates in Boolean function synthesis.

Contribution

The paper presents the NNGS algorithm with a controller component that can be adapted for related problems, and demonstrates its effectiveness with a proof-of-concept rule-based controller.

Findings

Outperforms recent algorithms in run times

Maintains comparable solution sizes

Achieves 100% success rate on Boolean benchmarks

Abstract

Generally, when genetic programming (GP) is used for function synthesis any valuable experience gained by the system is lost from one problem to the next, even when the problems are closely related. With the aim of developing a system which retains beneficial experience from problem to problem, this paper introduces the novel Node-by-Node Growth Solver (NNGS) algorithm which features a component, called the controller, which can be adapted and improved for use across a set of related problems. NNGS grows a single solution tree from root to leaves. Using semantic backpropagation and acting locally on each node in turn, the algorithm employs the controller to assign subsequent child nodes until a fully formed solution is generated. The aim of this paper is to pave a path towards the use of a neural network as the controller component and also, separately, towards the use of meta-GP as a mechanism for improving the controller component. A proof-of-concept controller is discussed which demonstrates the success and potential of the NNGS algorithm. In this case, the controller constitutes a set of hand written rules which can be used to deterministically and greedily solve standard Boolean function synthesis benchmarks. Even before employing machine learning to improve the controller, the algorithm vastly outperforms other well known recent algorithms on run times, maintains comparable solution sizes, and has a 100% success rate on all Boolean function synthesis benchmarks tested so far.