Evolving a Sort: Lessons in Genetic Programming (1993) [4 citations — 0 self]
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author = {Kenneth E. Kinnear Jr},
title = {Evolving a Sort: Lessons in Genetic Programming},
booktitle = {in Proceedings of the 1993 International Conference on Neural Networks},
year = {1993},
pages = {881--888},
publisher = {IEEE Press}
}
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Abstract:
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. I. INTRODUCTION Trying to evolve even as relatively simple an algorithm as a sorting routine can teach a number of useful lessons concerning the power as well as the pitfalls of using genetic techniques to directly evolve computer programs. A. Genetic Programming Programming paradigms that exploit evolutionary techniques are becoming mor...
