A Preliminary Exploration of Floating Point Grammatical Evolution

TL;DR

This paper introduces Floating Point Grammatical Evolution (FP-GE), a new framework that simplifies visualizing fitness landscapes and applying continuous search algorithms in genetic programming.

Contribution

It presents a novel FP-GE framework using a single floating point genotype, enabling easier visualization and integration with continuous optimization methods.

Findings

FP-GE allows visualization of fitness landscapes for GP.

The framework facilitates applying continuous search algorithms like Differential Evolution.

Experimental results compare search meta-heuristics on regression problems.

Abstract

Current GP frameworks are highly effective on a range of real and simulated benchmarks. However, due to the high dimensionality of the genotypes for GP, the task of visualising the fitness landscape for GP search can be difficult. This paper describes a new framework: Floating Point Grammatical Evolution (FP-GE) which uses a single floating point genotype to encode an individual program. This encoding permits easier visualisation of the fitness landscape arbitrary problems by providing a way to map fitness against a single dimension. The new framework also makes it trivially easy to apply continuous search algorithms, such as Differential Evolution, to the search problem. In this work, the FP-GE framework is tested against several regression problems, visualising the search landscape for these and comparing different search meta-heuristics.