Alan Blair
ABSTRACT
We explore the evolution of programs for control 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 HERCL programs can be evolved to robustly perform a benchmark double pole balancing task from a specified range of initial conditions, with the poles remaining balanced for up to an hour of simulated time.
- Blair, A., 2013. Learning the Caesar and Vigenere Cipher by Hierarchical Evolutionary Re-Combination, 2013 Congress on Evolutionary Computation, 605--612.Google Scholar
Cross Ref
- Gomez, F. and R. Miikkulainen, 2002. "Solving non-Markovian Control Tasks with Neuroevolution", 16th Int'l Jount Conf. on Artificial Intelligence, 1356--1361. Google ScholarDigital Library
- Minsky, Marvin. 1988 Society of mind, Simon & Schuster.Google Scholar
- Saravanan, N. and D. B. Fogel, 1995. "Evolving neural control systems", IEEE Expert 10(3), 23--27. Google ScholarDigital Library
- Stanley, K.O. and R. Miikkulainen, 2002. "Evolving neural networks through augmenting topologies", Evolutionary Computation 10(2), 99--127. Google ScholarDigital Library
- Wieland, A., 1991. "Evolving Neural Network Controllers for Unstable Systems", Int'l Joint Conf. on Neural Networks, 667--673.Google Scholar
Index Terms
- Incremental evolution of HERCL programs for robust control
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