Wallace

Named after Alfred Russel Wallace, because naming a package Darwin on iOS is a bad idea.

This is a command line tool to generate heterogenous groups of students, specifically for the X-HEC Entrepreneur Master. This package is split between Wallace, which contains code specific for the use case and wraps it around a command line interface, and the WallaceCore package, which is completely agnostic and could be applied to other similar problems.

Installation

1. Clone the repository
2. Run the following:

$ swift build -c release
$ cd .build/release
$ cp -f Wallace /usr/local/bin/wallace

Usage

All the commands support the following options:

• --group-size: The desired size of groups (default: 3)
• --population-size: The size of a population at a given generation (default: 10)
• --parent-count: Number of parents selected at a given generation (default: 5)
• --mutation-probability: Probability of a mutation happening on a child chromosome (default: 0.7)
• --max-generation: Max number of generations. Increase to run the algorithm longer (default: 1000)
• --debug: Enable debug logs

The first command to run is wallace jura-groups <file_url>, with a reference to a csv file containing the students. A file containing the groups will be outputed, and a file saving the students in an internal format will be saved.

You can then run in order

• wallace crea-groups which create groups for each minor (Deep Tech & High Touch)
• wallace redressement-groups
• wallace scale-up groups

Literature on genetic algorithms

A genetic algorithm is a search heuristic that is inspired by Charles Darwin’s theory of natural evolution. This algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction in order to produce offspring of the next generation.

The process of natural selection starts with the selection of fittest individuals from a population. They produce offspring which inherit the characteristics of the parents and will be added to the next generation. If parents have better fitness, their offspring will be better than parents and have a better chance at surviving. This process keeps on iterating and at the end, a generation with the fittest individuals will be found.

From: https://towardsdatascience.com/introduction-to-genetic-algorithms-including-example-code-e396e98d8bf3

Scholar articles:

• http://www.wseas.us/e-library/conferences/2012/Paris/ECCS/ECCS-14.pdf
• http://mat.uab.cat/~alseda/MasterOpt/GeneticOperations.pdf

Description of the chromosome

A chromosome represents the list of all the students. So if we represent each student by a number between 0 and N - 1, where N is the number of students, then each gene of the chromosome is a number between 0 and N - 1.

The group to which a student belongs is going to be its position in the chromosome. For example

 2 3 4 5 7 1 8 9 6
 1 1 1 2 2 2 3 3 3

The chromosome here is 234571896, and the first group is 2,3,4, the second group is 5,7,1 and the last group is 8,9,6

Description of the algorithm

Generate P chromosomes, this is the initial population. Currently random, but the initial population could be seeded with "good" chromosomes

While the generation has not reached the threshold
    Calculate the fitness of each chromosome in the population
    Select X parents with the best fit 

    Generate P - X offprings: 
        Select two parents Among the X and apply crossover
        
    For each offspring:
        With a probability p, apply a mutation 
    
    New population is the selected parents and the offsprings
    Generation is incremented

Return the fittest parent

Contributing

1. After making modifications, run the tests to make sure the core functionnality is still working
2. Build the tool with swift build
3. Test the commands with .build/debug/Wallace run-all promo2022.csv

Installing

make build

You can now use wallace as a standalone CLI.