Evolving Event-driven Programs with SignalGP

TL;DR

SignalGP introduces an event-driven genetic programming approach that automatically triggers program modules in response to environmental signals, enhancing reactive behavior in evolved programs.

Contribution

This work extends tag-based referencing techniques to an event-driven context within genetic programming, demonstrating improved reactive capabilities.

Findings

SignalGP outperforms sensor-based variants in reactive tasks

Event-driven paradigm enables rapid environmental interaction

Applicable to linear GP and potentially other GP forms

Abstract

We present SignalGP, a new genetic programming (GP) technique designed to incorporate the event-driven programming paradigm into computational evolution's toolbox. Event-driven programming is a software design philosophy that simplifies the development of reactive programs by automatically triggering program modules (event-handlers) in response to external events, such as signals from the environment or messages from other programs. SignalGP incorporates these concepts by extending existing tag-based referencing techniques into an event-driven context. Both events and functions are labeled with evolvable tags; when an event occurs, the function with the closest matching tag is triggered. In this work, we apply SignalGP in the context of linear GP. We demonstrate the value of the event-driven paradigm using two distinct test problems (an environment coordination problem and a distributed leader election problem) by comparing SignalGP to variants that are otherwise identical, but must actively use sensors to process events or messages. In each of these problems, rapid interaction with the environment or other agents is critical for maximizing fitness. We also discuss ways in which SignalGP can be generalized beyond our linear GP implementation.