A new genetic algorithm for solving optimization problems

TL;DR: In this article, the authors proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM), which proved to be efficient with better accuracy that can work on lightweight computational devices.

TL;DR: The comparisons show that the proposed EEGWO algorithm significantly improves the performance of GWO and offers the highest solution quality, strongest robustness, and fastest global convergence among all of the contenders on almost all ofThe test functions.

TL;DR: A new hybrid approach based on the thresholding technique to overcome ISP for COVID-19 chest X-ray images by integrating a novel meta-heuristic algorithm known as a slime mould algorithm (SMA) with the whale optimization algorithm to maximize the Kapur’s entropy.

TL;DR: A new initial population strategy has been developed to improve the genetic algorithm for solving the well-known combinatorial optimization problem, traveling salesman problem, by reconnecting each cluster based on the k-means algorithm.

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.

TL;DR: A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented and it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach.

TL;DR: In this article, the authors present a nonparametric statistical procedure for the test of sample normality in the context of practice questions, and compare the results of two related tests: the Mann-Whitney U -Test and the Fisher Exact Test.