Cohort Intelligence used to solve various mathematical functions

Overview

Cohort-Intelligence-for-Mathematical-Functions

About Cohort Intelligence :

Cohort Intelligence ( CI ) is an optimization technique. It attempts to model the behavior often observed in a self-organizing system in which candidates in a cohort interact and compete with one another in order to achieve shared goals. Each candidate tries to improve its own behavior by observing the behavior of every other candidate in that cohort. Each candidate in the cohort follows a certain behavior which may result in the improvement of its own behavior. When a candidate attempts to follow a given behavior characterized by certain qualities, it often adopts such qualities in a manner that may improve its own goal. In this way, candidates in the cohort learn from one another which, in time, helps improve the behavior of the entire group. The cohort’s behavior as a whole is said to have reached saturation (convergence) if, over a considerable number of learning attempts, the individual behavior of all candidates does not improve considerably making it difficult to distinguish between them. In other words, the difference between the individual behaviors of the candidates becomes insignificant.

Read more about CI here.

This repository contains the following functions solved using Cohort Intelligence :

  1. Colville Function
  2. Dixon and Price Function
  3. Sphere Function

1. Branin Function :

The following site provides data about Branin Function.

3D plot of Sphere Function :

The output of Branin Function using CI approach is provided below :

  • Output with 50 Learning Attempts :

  • Output with 500 Learning Attempts :

2. Colville Function :

The following site provides data about Colville Function.

3D plot of Colville Function :

The Global Minimum of Colville Function is obtained at f(x*) = 0, at x* = (1,1,1,1).

The output of Colville Function using CI approach is provided below :

3. Dixon and Price Function :

The following site provides data about Dixon and Price Function.

3D plot of Dixon and Price Function :

The output of Colville Function using CI approach is provided below :

4. Sphere Function :

The following site provides data about Dixon and Price Function.

3D plot of Sphere Function :

The output of Colville Function using CI approach is provided below :

Owner
Aayush Khandekar
Aayush Khandekar
Machine Learning e Data Science com Python

Machine Learning e Data Science com Python Arquivos do curso de Data Science e Machine Learning com Python na Udemy, cliqe aqui para acessá-lo. O prin

Renan Barbosa 1 Jan 27, 2022
Gaussian Process Optimization using GPy

End of maintenance for GPyOpt Dear GPyOpt community! We would like to acknowledge the obvious. The core team of GPyOpt has moved on, and over the past

Sheffield Machine Learning Software 847 Dec 19, 2022
Distributed Computing for AI Made Simple

Project Home Blog Documents Paper Media Coverage Join Fiber users email list Uber Open Source 997 Dec 30, 2022

K-Means clusternig example with Python and Scikit-learn

Unsupervised-Machine-Learning Flat Clustering K-Means clusternig example with Python and Scikit-learn Flat clustering Clustering algorithms group a se

Emin 1 Dec 13, 2021
Cryptocurrency price prediction and exceptions in python

Cryptocurrency price prediction and exceptions in python This is a coursework on foundations of computing module Through this coursework i worked on m

Panagiotis Sotirellos 1 Nov 07, 2021
LinearRegression2 Tvads and CarSales

LinearRegression2_Tvads_and_CarSales This project infers the insight that how the TV ads for cars and car Sales are being linked with each other. It i

Ashish Kumar Yadav 1 Dec 29, 2021
Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow...

Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow...

Thoughtworks 318 Jan 02, 2023
Learn how to responsibly deliver value with ML.

Made With ML Applied ML · MLOps · Production Join 30K+ developers in learning how to responsibly deliver value with ML. 🔥 Among the top MLOps reposit

Goku Mohandas 32k Dec 30, 2022
Deep Survival Machines - Fully Parametric Survival Regression

Package: dsm Python package dsm provides an API to train the Deep Survival Machines and associated models for problems in survival analysis. The under

Carnegie Mellon University Auton Lab 10 Dec 30, 2022
李航《统计学习方法》复现

本项目复现李航《统计学习方法》每一章节的算法 特点: 笔记摘要:在每个文件开头都会有一些核心的摘要 pythonic:这里会用尽可能规范的方式来实现,包括编程风格几乎严格按照PEP8 循序渐进:前期的算法会更list的方式来做计算,可读性比较强,后期几乎完全为numpy.array的计算,并且辅助详

58 Oct 22, 2021
Python package for machine learning for healthcare using a OMOP common data model

This library was developed in order to facilitate rapid prototyping in Python of predictive machine-learning models using longitudinal medical data from an OMOP CDM-standard database.

Sontag Lab 75 Jan 03, 2023
Iterative stochastic gradient descent (SGD) linear regressor with regularization

SGD-Linear-Regressor Iterative stochastic gradient descent (SGD) linear regressor with regularization Dataset: Kaggle “Graduate Admission 2” https://w

Zechen Ma 1 Oct 29, 2021
Katana project is a template for ASAP 🚀 ML application deployment

Katana project is a FastAPI template for ASAP 🚀 ML API deployment

Mohammad Shahebaz 100 Dec 26, 2022
Decision tree is the most powerful and popular tool for classification and prediction

Diabetes Prediction Using Decision Tree Introduction Decision tree is the most powerful and popular tool for classification and prediction. A Decision

Arjun U 1 Jan 23, 2022
PyTorch extensions for high performance and large scale training.

Description FairScale is a PyTorch extension library for high performance and large scale training on one or multiple machines/nodes. This library ext

Facebook Research 2k Dec 28, 2022
Machine Learning Course with Python:

A Machine Learning Course with Python Table of Contents Download Free Deep Learning Resource Guide Slack Group Introduction Motivation Machine Learnin

Instill AI 6.9k Jan 03, 2023
Fast Fourier Transform-accelerated Interpolation-based t-SNE (FIt-SNE)

FFT-accelerated Interpolation-based t-SNE (FIt-SNE) Introduction t-Stochastic Neighborhood Embedding (t-SNE) is a highly successful method for dimensi

Kluger Lab 547 Dec 21, 2022
An open-source library of algorithms to analyse time series in GPU and CPU.

An open-source library of algorithms to analyse time series in GPU and CPU.

Shapelets 216 Dec 30, 2022
High performance Python GLMs with all the features!

High performance Python GLMs with all the features!

QuantCo 200 Dec 14, 2022
Automatically create Faiss knn indices with the most optimal similarity search parameters.

It selects the best indexing parameters to achieve the highest recalls given memory and query speed constraints.

Criteo 419 Jan 01, 2023