Abstract
We have begun exploring code evolution by artificial economies. We implemented a reinforcement learning machine called Hayek2 consisting of agents, written in a machine language inspired by Ray’s Tierra, that interact economically. The economic structure of Hayek2 addresses credit assignment at both the agent and meta levels. Hayek2 succeeds in evolving code to solve Blocks World problems, and has been more effective at this than our hillclimbing program and our genetic program (GP). Our hillclimber and our GP also performed well, learning algorithms as strong as a simple search program that incorporates hand-coded domain knowledge. We made efforts to optimize our hillclimbing program and it has features that may be of independent interest. Our GP using crossover performed far better than a version utilizing other macro-mutations or our hillclimber, bearing on a controversy in the genetic programming literature.
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Baum, E.B., Durdanovic, I. (2002). Toward Code Evolution by Artificial Economies. In: Landweber, L.F., Winfree, E. (eds) Evolution as Computation. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55606-7_16
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DOI: https://doi.org/10.1007/978-3-642-55606-7_16
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