The report is located at bohacek_frantisek_report.pdf, it has
been generated from report.md

Rust has been used to write the library and program for solving
constrained problems. Cargo is used for managing the dependencies.

# How to run

To run the contr, go into the constr_hw02 folder, ie. `cd constr_hw02`,
then use `cargo run --release -- srank|nsga|nsga_improved|nsga_constr|nsga_multi problem`,
Supported problems are: `g04`, `g05` `g06`, `g08`, `g09`, `g11` and `g24`.

So for example: `cargo run --release -- srank g06`

# Solutions

The solution is then saved to `constr_hw02/solutions/<instance>`,
csv file with best candidate fitness (only feasible candidates
can be saved) and next to it is file with faction of feasible
candidates in each iteration.

# Plotting

The solutions might be plotted by using `py_plotter` program.
`cd py_plotter` and then `python3 plotter.py <json configuration>`,
The configurations are available in the same folder. For generating
the probability of success graphs, `target_proximity_plotter.py` is utilized.
It's usage is the same as `plotter.py`, but it uses different json fields.

The name of the plotter is same as used for hw01 as the plotting itself is the same.

# 'Reproducing' the plots in the report

To obtain the graphs in the report, use the following sequence of commands:

1. `compute.sh` to compute all the algorithms on all instances, 10 times for each instance.
2. Generate the plots, go to `py_plotter` folder, and run
```
python3 ./plotter.py config_feasible_g11.json
python3 ./plotter.py config_feasible_g06.json
python3 ./plotter.py config_feasible_g05.json
python3 ./plotter.py config_best_g09.json
python3 ./target_proximity_plotter.py config_target_proximity_comprehensive_no_std.json
```

Now all the csv solutions are in `constr_hw02/solutions` and all the plots are in
`py_plotter/plots`

The graphs won't be 1:1, because randomness is involved and the seeds haven't been captured
and aren't configurable.