@@ -7,27 +7,25 @@ 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.
The answer is simple: you should not.
Any implementation in scientific computing should be verified to be trusted.
## Test Functions
Many functions named "Test Functions" were proposed over time to test and compare multi-objective optimization algorithmls.
Many functions named "Test Functions" were proposed over time to test and compare multi-objective optimization algorithms.
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:
A test function is an function you can use as the problem to optimize, for which the expected results are known.
Test functions might 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.
- problems with constraints: a part of the space of solutions can not be computed, 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 in a part of the space of solutions which is heavily constrained, etc.
Test tunctions enable the testing of several aspects including:
The results of a test function are usually compared in both terms of:
- 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
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@@ -35,14 +33,22 @@ Test functions have another role: they constitute a simple example of optimizati
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 (you have these results in the [wikipedia page](https://en.wikipedia.org/wiki/Test_functions_for_optimization)).
- ConstrEx
- CP1
- Schaffer N1
- Schaffer N2
To run a test
- Choose one of the workflows starting with "test function"
- Run the workflow
- Update the view on the left using the "refresh" button
- Download the graph of the last Pareto front, and compare it with the literature (you might use the [wikipedia page](https://en.wikipedia.org/wiki/Test_functions_for_optimization) )
You might then tune the parameters of the optimization algorithm and analyze the results, to understand how to better use the optimization method.