Title

A Novel Generative Encoding For Exploiting Neural Network Sensor And Output Geometry

Keywords

Compositional pattern producing networks; HyperNEAT; Large-scale artifical neural networks; NEAT

Abstract

A significant problem for evolving artificial neural networks is that the physical arrangement of sensors and effectors is invisible to the evolutionary algorithm. For example, in this paper, directional sensors and effectors are placed around the circumference of a robot in analogous arrangements. This configuration ensures that there is a useful geometric correspondence between sensors and effectors. However, if sensors are mapped to a single input layer and the effectors to a single output layer (as is typical), evolution has no means to exploit this fortuitous arrangement. To address this problem, this paper presents a novel generative encoding called connective Compositional Pattern Producing Networks (connective CPPNs) that can effectively detect and capitalize on geometric relationships among sensors and effectors. The key insight is that sensors and effectors with consistent geometric relationships can be exploited by a repeating motif in the neural architecture. Thus, by employing an encoding that can discover such motifs as a function of network geometry, it becomes possible to exploit it. In this paper, a method for evolving connective CPPNs called Hypercube-based Neuroevolution of Augmenting Topologies (HyperNEAT) discovers sensible repeating motifs that take advantage of two different placement schemes, demonstrating the utility of such an approach. Copyright 2007 ACM.

Publication Date

8-27-2007

Publication Title

Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

Number of Pages

974-981

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/1276958.1277155

Socpus ID

34548105707 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/34548105707

This document is currently not available here.

Share

COinS