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Evolutionary algorithm toolbox and framework with high performance for Python
📊 An analytics server that doesn't undermine user's privacy
Next.js HOC to integrate Google Analytics on every page change
这是用C++写的遗传算法，参考《智能算法 30案例分析 第2版》一书，包含TSP、LQR控制器、结合量子算法、多目标优化、粒子群等，由于原作为matlab程序，综合自己思路通过C++写出来，算是练习和开个大坑
Genetic algorithm for walking simulation. Online demo: https://rossning92.github.io/genetic-algorithm/
使用vmd算法对含有噪声的图像信号进行分解，去除掉噪声信号，将剩余信号合成，得到去噪声图像。分别使用alo、ao、ga、gwo、mpa、spo、woa算法对vmd算法中的参数进行优化，实现快速、准确的完成图像信号的分解。
An Ensemble DL Model Tuned with Genetic Algorithm for Oil Production Forecasting.
Prediction google trace data using Functional Link Neural Network and Optimization Algorithms such as GA, PSO, ABC,...
Utilities for server side processing of Google Analytics in Deno CLI and Deploy
A library meant to assist during an Google Analytics implementation
Python implementation of Tabu Search (TB), Genetic Algorithm (GA), and Simulated Annealing (SA) solving Travelling Salesman Problem (TSP). Term project of Intelligent Optimization Methods, UCAS course 070105M05002H. 禁忌搜索, 遗传算法, 模拟退火解旅行商问题的Python实现. 中国科学院大学现代智能优化方法大作业.
Intrusion Detection is a technique to identify the abnormal behavior of system due to attack. The unusual behavior of the environment is then identified and steps are taken and methods are formed to classify and recognize attacks. Data set containing a number of records sometimes may decrease the classifiers performance due to redundancy of data. The other problems may include memory requirements and processing power so we need to either reduce the number of data or the number of records. Feature Selection techniques are used to reduce the vertical largeness of data set. This project makes a comparative study of Particle Swarm Optimization, Genetic Algorithm and a hybrid of the two where we see that PSO being simpler swarm algorithm works for feature selection problems but since it is problem dependent and more over its stochastic approach makes it less efficient in terms of error reduction compared to GA. In standard PSO, the non-oscillatory route can quickly cause a particle to stagnate and also it may prematurely converge on sub optimal solutions that are not even guaranteed to be local optimum. A further drawback is that stochastic approaches have problem-dependent performance. This dependency usually results from the parameter settings in each algorithm. The different parameter settings for a stochastic search algorithm result in high performance variances. In this project the modification strategies are proposed in PSO using GA. Experimental results show that GA performs better than PSO for the feature selection in terms of error reduction problems whereas hybrid outperforms both the model in terms of error reduction.
Evolving Architectures for Convolutional Neural Networks using the Genetic Algorithm
A library meant to assist during an Google Analytics implementation
