Abstract
We peruse the idea of algorithmic design through Darwinian evolution, focusing on the problem of evolving list search algorithms. Specifically, we employ genetic programming (GP) to evolve iterative algorithms for searching for a given key in an array of integers. Our judicious design of an evolutionary language renders the evolution of linear-time search algorithms easy. We then turn to the far more difficult problem of logarithmic-time search, and show that our evolutionary system successfully handles this case. Subsequently, because our setup might be perceived as being geared towards the emergence of binary search, we generalize our genomic representation, allowing evolution to assemble its own useful functions via the mechanism of automatically defined functions (ADFs). We show that our approach routinely and repeatedly evolves general and correct efficient algorithms.
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Wolfson, K., Sipper, M. (2010). Evolving Efficient List Search Algorithms. In: Collet, P., Monmarché, N., Legrand, P., Schoenauer, M., Lutton, E. (eds) Artifical Evolution. EA 2009. Lecture Notes in Computer Science, vol 5975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14156-0_14
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DOI: https://doi.org/10.1007/978-3-642-14156-0_14
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