@@ -32,3 +32,5 @@ All the accepted submissions will be part of the market place and will be compil

-[SimPopLocal model](simpoplocal): a geographical model calibrated using genetic algorithms.

- [Sensitivity-Screening analysis](sensitivity/morris): a method to quickly analyze which inputs are influential on large spaces of parameters.

- [Global Sensitivity Analysis](sensitivity/saltelli): a variance based sensitivity analysis of model output.

-[Test Functions for NSGA2](nsga2-test-functions): reference functions to double check the correctedness of the NSGA2 algorithm, and also view examples of usage of NSGA2

Metaheuristics for optimization such as genetic algorithms leave a huge freedom to developers for their implementation.

Genetic Algorithms such as NSGA2 are even less trivial.

Their implementation in the [mgo](https://github.com/openmole/mgo) used in [OpenMole](https://openmole.org/) was adapted so they are efficient for computation on clusters or grids.

This leaves a lot of uncertainty to the user on what really happens.

Why should you, as a user, trust this implementation?

The answer is simple: you should not.

Any implementation in scientific computing should be verified.

## Test Functions

Many functions named "Test Functions" were proposed over time to test and compare multi-objective optimization algorithmls.

The [wikipedia page](https://en.wikipedia.org/wiki/Test_functions_for_optimization) lists several of them.

A test function is an optimization function which you can propose as the problem to optimize to your implementation.

Functions test:

- problems with multiple parameters and multiple objectives

- problems with constraints: a part of the space of parameter leads to computation errors as a result, so the algorithm should achieve to deal with it

- problems with parts of the space of solution that are tricky to detect, either because they are statistically unlikely to find, or because they are is a part of the space of solutions which is heavily constrained, etc.

Test tunctions enable the testing of several aspects including:

- coverage of the Pareto front,

- speed of convergence

Once a test function was explored by an implementation of an algorithm, you should compare the Pareto front obtained withou your implementation with the one expected in literature.

## Test and Learn

Test functions have another role: they constitute a simple example of optimization problem.

As a user, you can also learn to tune the parameters of the genetic algorithm as check when convergence

occurs.

## Test Workflows for OpenMole

We provide here a few examples of test functions which you can open with OpenMole.

For each workflow:

- Choose a test function.

- Run the workflow.

- Open the files with the solutions, check you understand them.

- Graph them and compare them with the literature.