Hacker News with Generative AI: Evolutionary Algorithms

Neuroevolution of augmenting topologies (NEAT algorithm) (wikipedia.org)
NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Kenneth Stanley and Risto Miikkulainen in 2002 while at The University of Texas at Austin.
Artificial Intelligence, Machine Learning, Evolutionary Algorithms, Neural Networks, Genetics
91 points by elsewhen 47 days ago | 33 comments
Low-poly image generation using evolutionary algorithms in Ruby (2023) (thomascountz.com)
Inspired by biological systems, evolutionary algorithms model the patterns of multi-generational evolution in order to unearth unique ideas. They work by generating a vast number of potential solutions to a particular problem and then pitting them against each other in a process akin natural selection: only the fittest survive. In this way, evolutionary algorithms are able to navigate large ambiguous search spaces in order to find solutions to problems that may be difficult or inefficient to solve using other methods.
Generative AI, Evolutionary Algorithms, Ruby, Image Generation
118 points by thomascountz 74 days ago | 42 comments
Supporting game design with evolutionary algorithms (gamedeveloper.com)
Game Design, Evolutionary Algorithms
75 points by kevthecoder 155 days ago | 57 comments
Darwin Machines (vedgie.net)
Artificial Intelligence, Machine Learning, Evolutionary Algorithms
164 points by calepayson 185 days ago | 82 comments