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## Quick Howto

To tackle optimization problems already existing in Matlab, we provide a simple Matlab interface comprising in a Matlab class definition. The Matlab m-files are located in the EvA2 resources folder of the distributed binary jar or can be downloaded from the homepage. If you have an existing Matlab function you want to optimize with some standard algorithms implemented in EVA2, you can do this directly from your Matlab console.

A Matlab function, e.g., testfun.m , as in Alg. 1 will be used to repeatedly evaluate candidate solutions. Three basic data types are designated for use with the Matlab interface, denoted by flags 'binary', 'int', or 'double'. Depending on the data type flag, a binary, integer, or real-valued individual type will be used. The matlab function will receive a char of symbols '0'/'1' or an integer array or a double array, respectively.

To run an optimization from Matlab, look at the following steps. For explicit examples, look at Algs. 2-4 and the exemplary target function listed in Alg. 1.

1. Download the EvA2Base.jar and add it to the Matlab classpath, e.g. by typing javaaddpath '/home/username/EvA2Base.jar' in the Matlab console.
2. Extract the Matlab JEInterface code to your Matlab working directory within its own class directory @JEInterface''.
3. Define the range of your search space using a matrix consisting of the lower and upper bounds of the allowed space, e.g. enter R=[-5 -5 -5;5 5 5] to define a 3-dimensional real valued search space with bounds -5/5 in each dimension. For binary problems, R should be a positive integer, e.g., R=20 for a binary problem with 20 bits.
4. For a target function testfun.m to be minimized, create a JEInterface in Matlab by typing, for example: JI=JEInterface(@testfun, 'double', R). Notice that testfun.m must be accessible from your working directory, it should not be placed in the @JEInterface directory. For integer or binary target functions, replace 'double' by 'int' or 'binary', respectively.
5. To view the possible optimization strategies, type showOptimizers(JI).
6. You can now select an optimizer and use its ID to start the optimization, e.g. JI=optimize(JI,1) for a standard ES.
7. Wait for the optimization to finish and type getResult(JI) to get the best solution found.    Next: Details on JEInterface Up: Optimization from Matlab Previous: Optimization from Matlab   Contents
Marcel Kronfeld 2011-05-05