Class-Sync Generator

This is the all new addition to the classsync where we are introducing the automation in timetable generation using genetic algorithm.
(currently in the intitial-coding-ish phase of development)

Languages, Frameworks and Tools

Data and Algorithm Flow

Step	Description
Data Collection	Gather all necessary data including teacher availability, room availability, and subject requirements.
Initial Population Generation	Create an initial set of possible timetables (population).
Fitness Evaluation	Evaluate each timetable based on predefined criteria such as teacher conflicts, room conflicts, and adherence to subject requirements.
Selection	Select the best-performing timetables to serve as parents for the next generation.
Crossover and Mutation	Generate new timetables by combining parts of the selected parents and introducing small random changes (mutations).
Iteration	Repeat the evaluation, selection, and crossover/mutation steps for several generations until an optimal timetable is found.
Final Timetable	Select the best timetable from the final generation as the solution.

This process ensures that the generated timetable is optimized for the given constraints and requirements.

Data Structure Used

data.JSON (example)
{
    "fitness": 0,
    "data":[
        {
            "local_fitness": 0,
            "timetable": [
                [{ "classid": "", "teacherid": "" }, ........ for 10 periods ],
                [{ "classid": "", "teacherid": "" }, ........ for 10 periods ],
                "
                "
                "
                .... for 7 days a week 
            ],
            "subjects": [
                {
                    "subjectid": "TEST1",
                    "teacherid": "2118526",
                    "weekly_hrs": 3,
                    "type": "theory"
                },
                {
                    "subjectid": "PTEST1",
                    "teacherid": "21185299",
                    "weekly_hrs": 2,
                    "type": "practical"
                },
                ... as many subject as we want to plot
            ]
        },
        {
            "local_fitness": 0,
            "timetable": [
                [{ "classid": "", "teacherid": "" }, ........ for 10 periods ],
                "
                "
                .... for 7 days a week 
            ],
            "subjects": [
                {
                    "subjectid": "TEST1",
                    "teacherid": "2118526",
                    "weekly_hrs": 3,
                    "type": "theory"
                },
                ... as many subject as we want to plot
            ]
        },
        .... as many sections as we need with predecided teacher and subjects 
    ]
}

This is an abstract representation of our data strucutre as timetable set

File Structure *(Not updated)

File Name	Description
test.js	Function to build a generation
plot_timetables.js	Function that plot the timetable according to the predefined resource data set
validate_timetable.js	Function that validate the timetable set from teacher and room clash
fitness_func.js	Fitness Function
selection.js	Function that do the selection of the Fit timetable for next generation as parents
data.json	Represent a set of timetables (1 person in population)
data2.json	Represent a plotted set of timetables (for testing)
population.json	Represent population of many set of timetable
room.json	Room data in which slots can be plotted
population_selected.json	Selected timetable from this generation
index.html	For visualization of plotted set of timetable
index.js index2.js index3.js	JS for index.html
README.md	Readme.md

Penalty and Reward System *(Not updated)

(these are not used right now)

Type	Condition	Points
Teacher Conflict	Per conflict	-20
Room Conflict	Per conflict	-20
Teacher Overload	Per extra hour	-10
Student Overload	For exceeding max classes in a day	-5
Perfect Day Reward	4-5 classes in a day	+6
Active Day Count	4-5 active days	+20
Low Active Days	Less than 3 active days	-10

Terminology

Algorithm Terminology	Common Terminology	Description	Example
Generation	Population	Represent a Set of probable group of Timtable (or genome)	20 set of 20 section's Timetable (20 Genomes)
Genome or Chromosome	Timetable Set or Person (or unit) of a generation	It is the Set of probable Timtables (or Gene)	20 section's Timetable (20 Genes)
Gene	Timtable	It is the single Timetable	Timetable of section A