“Genetic” Programming
نویسندگان
چکیده
Much of evolutionary computation was inspired by Mendelian genetics. But modern genetics has since advanced considerably, revealing that genes are not simply parameter settings, but interactive cogs in a complex chemical machine. At the same time, an increasing number of evolutionary computation domains are evolving non-parameterized mechanisms such as neural networks or symbolic computer programs. As such, we think modern biological genetics offers much in helping us understand how to evolve such things. In this paper, we present a gene regulation model for Drosophila melanogaster. We then apply gene regulation to evolve deterministic finite-state automata, and show that our approach does well compared to past examples from the literature. 1 EVOLUTIONARY COMPUTATION
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