Genetic algorithms are unique ways to solve complex problems by harnessing the power of nature. By applying these methods to predicting security prices, traders can optimize trading rules by ...

[***JMLR-2024***] PyPop7: A Pure-Python Library for POPulation-based Black-Box Optimization (BBO), especially *Large-Scale* versions/variants (e.g., evolutionary ...

Genetic algorithm is widely used in multi-objective mechanical structure optimization. In this paper, a genetic algorithm-based optimization method for ladle refractory lining structure is proposed.

A C++ platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.

Scientists in Spain have used genetic algorithms to optimize a feedforward artificial neural network for the prediction of energy generation of PV systems. Genetic algorithms use “parents” and ...

Candidate genetic variants included in a genetic risk algorithm may not meet standards of reasonable clinical efficacy in identifying opioid use disorder risk, according to a study published in JAMA ...

Abstract: This paper presents a novel knowledge-based genetic algorithm to generate a collision-free path in complex environments. The proposed algorithm infuses specific domain knowledge into robot ...

In this letter, we propose a genetic algorithm (GA) based channel parameter estimation method for closely-spaced multipath environment. The paths' parameters are first encoded into binary code space ...

A new study suggests clinical utility of the first FDA-approved genetic test to help gauge an individual’s risk for opioid ...

AI-driven genomics startups leverage technology to analyze genetic data, predict predispositions, and offer personalized ...