Abstract
The genetic programming of iterative concurrent
programs written in the CCS process algebra is
investigated. Using a generational genetic programming
scheme, experiments succesfully evolved a cyclic
concurrent program that performs a even-parity-2
analysis on a communicating input stream. The fitness
evaluation strategy determines how well programs
communicate with randomly generated streams of input
signals. Fitness is measured by performing a pairwise
sequence alignment comparison of two execution
sequences -- the output sequence generated by the
program communicating with the test signal stream, and
the correct output sequence for that test case. The
optimal edit distance between these sequences is
efficiently computed using dynamic programming. The
main result is that sequence alignment evaluation
against randomly generated test cases is a promising
evaluation strategy for evolving cycling protocols.
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