Hands-On Genetic Algorithms with Python by Eyal Wirsansky is a new book which explores the world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine ...

The approach combines a new genetic algorithm using empirical interatomic potentials to explore the configurational phase space of boundaries, and thereafter refining structures and free energies ...

Researchers tested phononic nanomaterials designed with an automated genetic algorithm that responded to light pulses with controlled vibrations. This work may help in the development of next ...

We describe a genetic algorithm (GA) for designing efficient search paths, against a moving target, in complicated environments. The immediate application is acoustic search for submarines, but the ...

This approach enables the user to select the most appropriate study for each risk factor, for example, using genetic estimates from large meta-analyses of genome-wide association studies, and ...

[Kory] has been experimenting with genetic algorithms. Normally we’d expect his experiments to deal with tuning the variables in a control system or something, but he’s doing something … ...

We present a genetic algorithm for the multiple-choice integer program that finds an optimal solution with probability one (though it is typically used as a heuristic). General constraints are relaxed ...

By analyzing thousands of genetic risk variants, a new algorithm may be able to predict your risk of developing chronic kidney disease decades before onset.

The primary ethical problems with CRISPR in humans so far have come from “germ line” genetic engineering, that is, editing the genes of gametes or early embryos so that the alterations pass ...