Amazon.com: Juan Farin: An LLM Awakens eBook : Sipper, Moshe: Kindle Store

Amazon.com: Juan Farin: An LLM Awakens eBook : Sipper, Moshe: Kindle Store

Amazon.com

Amazon.com

Modern Biblical Tales - ILLUMINATION - Medium

God intends to smite the evil town of Sodom. But Abe persuades Him to make a deal: if ten good people can be found, the smiting will be called off. Enter Jimmy Spake, an intrepid private investigator…

Dragon in Distress - The Haven - Medium

No, no, please don’t run away to seek cover. Bob was a very nice dragon: He helped little old ladies cross the street, he taught boy scouts how to light campfires, and from time to time he even lent…

Pee Review: The Enshittification of Science? - Cantor’s Paradise

Enshittification is an interesting term recently coined by writer Cory Doctorow with respect to online platforms: While Doctorow was aiming at online platforms, I’d like to apply this term to…

Fitness Approximation Through Machine Learning with Dynamic Adaptation to the Evolutionary State

We present a novel approach to performing fitness approximation in genetic algorithms (GAs) using machine learning (ML) models, focusing on dynamic adaptation to the evolutionary state. We compare different methods for (1) switching between actual and approximate fitness, (2) sampling the population, and (3) weighting the samples. Experimental findings demonstrate significant improvement in evolutionary runtimes, with fitness scores that are either identical or slightly lower than those of the fully run GA—depending on the ratio of approximate-to-actual-fitness computation. Although we focus on evolutionary agents in Gymnasium (game) simulators—where fitness computation is costly—our approach is generic and can be easily applied to many different domains.