Evolving a sort: lessons in genetic programming

In applying the genetic programming paradigm to the task of evolving iterative sorting algorithms, a variety of lessons are learned. With proper selection of the primitives, sorting algorithms are evolved that are both general and non-trivial. The sorting problem is 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 is created, i.e., nonfitness single cross-over. It shows promise in at least this environment.<<ETX>>

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