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copied!<p>Basically, yes! The parameters of your score function could be the parameters that your GA is going to evolve. You may want to use a real coded genetic algorithm or evolution strategy (CMA-ES) if your parameters are in the real domain.</p> <p>There exist several possible choices for crossover:</p> <ul> <li>Average / Intermediate</li> <li>Blend-Alpha (BLX-a)</li> <li>Blend-Alpha-Beta (BLX-a-b)</li> <li>Discrete</li> <li>Heuristic</li> <li>Local</li> <li>Random Convex</li> <li>Simulated Binary (SBX)</li> <li>Single Point</li> </ul> <p>And also some mutation operators:</p> <ul> <li>Normal distributed N(0, sigma) -> e.g. with adaptation to reduce sigma over time</li> <li>Uniform distributed (in some positions)</li> <li>Polynomial mutation</li> </ul> <p>Another metaheuristic suitable for real coded problems is particle swarm optimization (PSO).</p> <p>With genetic programming you're going to evolve a formula (e.g. a tree). I'm not so sure why you mention it, maybe I still misunderstand something. Clarify your problem, just in case.</p> <p>EDIT:</p> <p>Okay it seems it's not the weights that you want to optimize, but the whole formula. Still, genetic algorithms can be used for this representation as well. I want to mention <a href="http://dev.heuristiclab.com" rel="nofollow">HeuristicLab</a> due to its good support for genetic programming.</p> <p>I assume you have a more complex problem since you want to optimize the scoring function, and still have another algorithm for optimizing the placement according to that scoring function. You could try an easy approach and generate a dataset with several positions predefined and the features calculated accordingly. Then you could formulate a classification problem and find a model that allows you to identify those positionings that are optimal.</p>

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