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copied!<p>Keep in mind that for real time genetic algorithms to be applicable you are generally better off starting by gathering a consistent training set of adversarial strategies (better if from different people) and using this set to evaluate fitness in the background, simulating games in fast-forward, to obtain in reasonable time a number of decent strategies you can start with (it's unthinkable to get humans to evaluate fitness of random strategies - would most likely take too long). A classic example implemented like this is the game of checkers, but also more complex games can leverage the same strategy (there is a famous example of futuristic naval battle where GA aided strategy defeated human opponents - can't remember details but I'll look it up and edit). Once you evolved a set of decent strategies, you could fire off the real time GA so that it keeps learning from humans. </p> <p>Also keep into account this could be an extremely slow process and there might be no value in using real time genetic algorithms, in the sense that you're better off collecting strategies from human opponents and running evolution in the background, so that the next time the same opponent plays you might have evolved a strategy capable of defeating him. Unless you've got loads of people playing - in that case it might make sense, but if the targets is to challenge people with increasingly good bots that's what I would do. Having people evaluating fitness of tentative strategies could be 1) slow 2) boring for them.</p> <p>A few papers dealing with real time genetic algorithms (worth skimming through the abstract and see if you're interested):</p> <ul> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.130.3277&rep=rep1&type=pdf" rel="nofollow noreferrer">GA for complex real time scheduling</a> </li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.13.1884&rep=rep1&type=pdf" rel="nofollow noreferrer">Aircraft landing scheduling with real time GA</a> </li> <li><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.129.4615&rep=rep1&type=pdf" rel="nofollow noreferrer">Musical Improvisation with real time GA</a></li> <li><a href="http://www.sersc.org/journals/IJUNESST/vol1_no1/papers/02.pdf" rel="nofollow noreferrer">Recommendation engine with real time GA</a></li> </ul> <p>Also worth mentioning, something similar has been done before by using neuroevolution (neural networks evolution with GAs, <a href="http://www.cs.ucf.edu/~kstanley/neat.html" rel="nofollow noreferrer">NEAT</a> is a good example and has been used for FPS afaik) but the considerations in terms of training set still stand valid.</p>

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