The Genetic Algorithm ("genetic algorithms" is actually a misnomer) actually has little to do with biology except for original inspiration. It is simply one of

The Genetic Algorithm ("genetic algorithms" is actually a misnomer) actually has little to do with biology except for original inspiration. It is simply one of a number of sample-based methods for doing global stochastic optimization. There's an old saying about the Genetic Algorithm: that it's the "third best way to do anything". Stochastic optimization methods (or "metaheuristics") in general are knowledge-poor methods, essentially last-ditch techniques where you don't have any other known way to solve your problem and you don't want to jump off the cliff into random or brute-force search. They rely on a central heuristic: that similar candidate solutions will likely have similar performance (the "smoothness" criterion). Here's the thing. There are a huge and growing number of crunchy problems in this category. If you're trying to find a good tic-tac-toe solution, you can almost certainly do better than a stochastic optimization method (just use state-space search, say). But if what if you're trying to find the set of behaviors for a two-thousand-agent multiagent simulation model which produces statistics most closely resembling known historical data? Or what if you're trying to find the best parameters for optimizing an aircraft engine whose space is filled with local optima? It's ugly problems like these, for which there is no principled solution method, where the Genetic Algorithm and its ilk reign supreme. You might say that you never see problems in computer science which are "best solved using genetic algorithms". My answer is: your daily problems are too simple to need them. Book plug: you might enjoy my free online text on the subject, called Essentials of Metaheuristics. You can also get it in paperback. http://cs.gmu.edu/~sean/book/metaheuristics/ http://cs.gmu.edu/~sean/book/metaheuristics/