SignalGP-Lite: Event Driven Genetic Programming Library for Large-Scale Artificial Life Applications

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@Misc{DBLP:journals/corr/abs-2108-00382,

• author = "Matthew Andres Moreno and Santiago Rodriguez Papa and Alexander Lalejini and Charles Ofria",
• title = "{SignalGP-Lite}: Event Driven Genetic Programming Library for Large-Scale Artificial Life Applications",
• howpublished = "arXiv",
• volume = "abs/2108.00382",
• year = "2021",
• month = "1 " # aug,
• keywords = "genetic algorithms, genetic programming",
• URL = "https://arxiv.org/abs/2108.00382",
• eprinttype = "arXiv",
• eprint = "2108.00382",
• timestamp = "Thu, 05 Aug 2021 01:00:00 +0200",
• biburl = "https://dblp.org/rec/journals/corr/abs-2108-00382.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• size = "28 pages",
• abstract = "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.",

}

Genetic Programming entries for Matthew Andres Moreno Santiago Rodriguez Papa Alexander Lalejini Charles Ofria

Citations