When using a genetic algorithm, it is important to carefully set the population size and the mutation and crossover rate. A small population size, or a high mutation rate, might prevent the ...

Genetic algorithms are ... represent the reproduction and crossover seen in biology, whereby a child takes on certain characteristics of its parents. Mutations represent biological mutation ...

Abstract: This paper presents the design and implementation of a novel matrix-friendly genetic algorithm (MGA) based population input memristor circuit. Selection, crossover and mutation operations ...

A study in the journal Pharmaceuticals has uncovered how specific genetic mutations in α2δ-1 and α2δ-3 proteins linked to autism spectrum disorders (ASD) alter neuronal functionality.

Abstract: The twin delayed deep deterministic policy gradient (TD3) algorithm and genetic (G) algorithm can take significant time to converge. Hence, it would be interesting to propose an alternative ...

In the subgroup with 17p deletion or TP53 mutation, zanubrutinib outperformed others with the most favorable HR (0.52) and the highest SUCRA value of 97%. Genetic mutations should be considered when ...

Radiation-induced mutation is unlikely to have induced genetic differences between dog populations in Chornobyl City and the nearby Chornobyl Nuclear Power Plant. Radiation-induced mutation is ...

This project explores the implementation of a Genetic Algorithm to train a virtual self-driving car with a simple neural network, focusing on the crossover operator, entirely in vanilla JavaScript (no ...