Some notebooks

Jupyter/IPython notebooks about evolutionary computation.

Genetic Algorithms notebooks - The Traveling Salesman Problem

( 🦉 ) This code lab intended to introduce new Machine Learning Algorithm // DEAP : Distributed Evolutionary Algorithm Framework.

Example code of a group of bio-inspired algorithms using jupyter notebooks and python

ai python notebooks

Projects and colab worksheets of Python -programming, Deep learning for Computer-vision, Data-mining, Data-analysis and Advanced optimization techniques

Curated collection of notebooks and code files I have worked on while learning a wide range of data science subfields, such as Reinforcement Learning, Natural Language Processing, Deep Neural Networks, Genetic Algorithms, etc. Some of these are accompanied by a pdf and/or article.

The notebook contains the description and implementation of 2 search algorithms:

Contains notebook implementations for the AI based assignments using graph based algorithms that are commonly used in solving AI based problems. Algorithms include BFS, DFS, Hill Climbing, Differential Evolution, Genetic, Back Tracking..

Notebooks of different Machine Learning programs and algorithms ranging from extremely basic to intermediate.

Github repo for the submission of the codes and notebooks for the LSN course at UNIMI

Kaggle competition santa-workshop-tour-2019 : genetic algorithm proposition compare to Guropi MIP optimizer

The travelling salesman problem (TSP) asks the following question: "Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city?" In this notebook, I demonstate the solution of this problem with the genetic algorithm.