pablormier

import numpy as np

def de(fobj, bounds, mut=0.8, crossp=0.7, popsize=20, its=1000):
    dimensions = len(bounds)
    pop = np.random.rand(popsize, dimensions)
    min_b, max_b = np.asarray(bounds).T
    diff = np.fabs(min_b - max_b)
    pop_denorm = min_b + pop * diff
    fitness = np.asarray([fobj(ind) for ind in pop_denorm])
    best_idx = np.argmin(fitness)

sergicastellasape

Title	Tweets	Citations	Organization	Country	Org Type
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models		1331	DeepMind, European Molecular Biology Laboratory	UK	academia
ColabFold: making protein folding accessible to all		1138	Harvard University, Max Planck Institute for Multidisciplinary Sciences, Michigan State University, Seoul National University, University of Tokyo	Germany, Japan, South Korea, USA	academia
A ConvNet for the 2020s	857	835	Meta, UC Berkeley	USA	industry
Hierarchical Text-Conditional Image Generation with CLIP Latents	105	718	OpenAI	USA	industry
PaLM: Scaling Language Modeling with Pathways	445	426	Google	USA	industry
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding	2462	390	Google	USA	industry
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding	11	342	NVIDIA	USA	industry
SignalP 6.0 predicts all five types of signal peptides using protein language models		2