moPepGen leverages a graph-based approach to improve the detection of hidden protein variants in a computationally efficient manner.

Some lucky people have rare genetic mutations that enable them to feel well-rested after just four hours of sleep, while the rest of us need around eight hours to function.

To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome ...

Genetic Algorithm (GA) [14 – 17] originated from computer simulation studies on biological systems and is a stochastic global search optimization method.It simulates the phenomena of replication, ...

Genetic algorithms evaluate potential solutions by evolving them over many generations and keeping the ones which work best each time.

This algorithm combines evolutionary search with real-coded crossover and matrix binary-coded crossover as genetic operators, creating a hybrid approach. The aim of the designed system is to assist ...

Luke Fox: The goal of using the genetic algorithm was to create an optimal massing that prioritized the creation of a central public square and maximized views for the building's occupants.

Ahima, M. (2019) A Study of Genetic Algorithm and Crossover Techniques. International Journal of Computer Science and Mobile Computing, 8, 335-344.