Transgenic evolution for classification tasks with hercl
We explore the evolution of programs for classification tasks, using the recently introduced Hierarchical Evolutionary Re-Combination Language (HERCL) which has been designed as an austere and general-purpose language, with a view toward modular evolutionary computation, combining elements from Linear GP with stack-based operations from forth. We show that evolved HERCL programs can successfully learn to perform a variety of benchmark classification tasks, and that performance is enhanced by the sharing of genetic material between tasks.
Full citation [Harvard style]:
Blair, A. D. (2015)., Transgenic evolution for classification tasks with hercl, in M. Randall (ed.), Artificial life and computational intelligence, Dordrecht, Springer, pp. 185-195.
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