https://github.com/jeugregg/santa-workshop-tour-2019

Kaggle competition santa-workshop-tour-2019 : genetic algorithm proposition compare to Guropi MIP optimizer
https://github.com/jeugregg/santa-workshop-tour-2019

competition cpp genetic-algorithm gurobi ipynb kaggle notebook numba santa

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

• Host: GitHub
• URL: https://github.com/jeugregg/santa-workshop-tour-2019
• Owner: jeugregg
• Created: 2020-01-08T09:35:12.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-04-22T00:53:28.000Z (about 5 years ago)
• Last Synced: 2025-01-12T18:35:41.877Z (5 months ago)
• Topics: competition, cpp, genetic-algorithm, gurobi, ipynb, kaggle, notebook, numba, santa
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/jeugregg/santa-s-2019-original-genetic-algorithm-method
• Size: 16.8 MB
• Stars: 1
• Watchers: 3
• Forks: 0
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

        
# santa-workshop-tour-2019

 Kaggle competition santa-workshop-tour-2019 using a original development of Genetic Algorithm

 

 My shared Notebook on Kaggle : https://www.kaggle.com/jeugregg/santa-s-2019-original-genetic-algorithm-method 

 

 Competition : https://www.kaggle.com/c/santa-workshop-tour-2019/

 

 This competition is about schedule optimization.

 I compare my original method and Gurobi optimizer method.

 You can read my report & presentation (in French) in [/doc/ folder](https://github.com/jeugregg/santa-workshop-tour-2019/tree/master/doc)

 

santa-s-2019-starter-notebook.ipynb  notebook contains :

    main fonction to optimize schedule of santa tour 2019 KAGGLE competition : (Loop over Generation part)

    a lot of sub-functions used by genetic algo main in Useful functions, Prepare mutation and Cost function parts.

    cost function optim is based on kaggle kernel: https://www.kaggle.com/xhlulu/santa-s-2019-faster-cost-function-24-s

    C++ Stochastic Product Search is base on kaggle kernel : https://www.kaggle.com/golubev/c-stochastic-product-search-65ns & https://www.kaggle.com/dmintry/c-stochastic-product-search-in-few-threads

    Most of the python functions are numba optimized

	VERSIONS :

    V3.2: 15/02/2020 : fixe sps add-on extend range choices

    V3.1: 15/02/2020 : impove sps add-on

    V3.0: 12/02/2020 : add stochastic product search boost

    V2.1: 04/02/2020 : correction fun_vect_mut

    V2.0: 03/02/2020 : random path population generation

    V1.1: 03/02/2020 : @njit(parallel=True)

    V1.0: 03/02/2020 : update generate_crossing_prob (optimize perf.)

    

santa_gurobi_01.ipynb : Notebook about  Test Gurobi MIP Optimizer

	

	 Reused with different seeds from :  

	 https://towardsdatascience.com/helping-santa-plan-with-mixed-integer-programming-mip-1951386a6ba5