Evolving a Sort: Lessons in Genetic Programming
نویسنده
چکیده
In applying the Genetic Programming paradigm to the task of evolving iterative sorting algorithms, a variety of interesting lessons were learned. With proper selection of the primitives, sorting algorithms were evolved that are both general and non-trivial. The sorting problem was then used as a testbed to evaluate the value of several alternative parameters, with some small gains shown. The value of applying Steady State Genetic Algorithm techniques to Genetic Programming, called Steady State Genetic Programming is demonstrated. One unusual genetic operator was created, non-fitness single crossover, which shows promise in at least this environment.
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