  Event-driven genetic programming representations have been shown to
outperform traditional imperative representations on interaction-intensive
problems. The event-driven approach organizes genome content into modules that
are triggered in response to environmental signals, simplifying simulation
design and implementation. Existing work developing event-driven genetic
programming methodology has largely used the SignalGP library, which caters to
traditional program synthesis applications. The SignalGP-Lite library enables
larger-scale artificial life experiments with streamlined agents by reducing
control flow overhead and trading run-time flexibility for better performance
due to compile-time configuration. Here, we report benchmarking experiments
that show an 8x to 30x speedup. We also report solution quality equivalent to
SignalGP on two benchmark problems originally developed to test the ability of
evolved programs to respond to a large number of signals and to modulate signal
response based on context.
