Evolving Event-driven Programs with SignalGP
This work addresses the problem of developing more efficient reactive programs for researchers in evolutionary computation and AI, though it is incremental as it builds on existing tag-based referencing techniques.
The authors tackled the challenge of evolving reactive programs by introducing SignalGP, a genetic programming technique that incorporates event-driven programming, and demonstrated its value by showing it outperforms sensor-based variants in environment coordination and distributed leader election problems, achieving higher fitness scores.
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.