Public articles linked to the same research event.
arXiv The work interprets several evolutionary algorithms as approximate Markov Chain Monte Carlo (MCMC) and, on that basis, introduces Distribution Matching Evolutionary Algorithms (DME), a class of search methods that sample from a global target distribution without updating model weights and that empirically shows higher sample efficiency than existing methods on problems requiring many samples to find a solution.
The work interprets several evolutionary algorithms as approximate Markov Chain Monte Carlo (MCMC) and, on that basis, introduces Distribution Matching Evolutionary Algorithms (DME), a class of search methods that sample from a global target distribution without updating model weights and that empirically shows higher sample efficiency than existing methods on problems requiring many samples to find a solution.
The work interprets several evolutionary algorithms as approximate Markov Chain Monte Carlo (MCMC) and, on that basis, introduces Distribution Matching Evolutionary Algorithms (DME), a class of search methods that sample from a global target distribution without updating model weights and that empirically shows higher sample efficiency than existing methods on problems requiring many samples to find a solution.
The work interprets several evolutionary algorithms as approximate Markov Chain Monte Carlo (MCMC) and, on that basis, introduces Distribution Matching Evolutionary Algorithms (DME), a class of search methods that sample from a global target distribution without updating model weights and that empirically shows higher sample efficiency than existing methods on problems requiring many samples to find a solution.