### added test functions for NSGA2 with BK, ConstrEx, Schaffer N1 and N2

parent 871a8c02
 ... ... @@ -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 ... ... @@ -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.