Evolving Open Complexity
Abstract
Information theoretic analysis of large evolved programs produced by running genetic programming for up to a million generations has shown even functions as smooth and well behaved as floating point addition and multiplication loose entropy and consequently are robust and fail to propagate disruption to their outputs. This means, while dependent upon fitness tests, many genetic changes deep within trees are silent. For evolution to proceed at reasonable rate it must be possible to measure the impact of most code changes, yet in large trees most crossover sites are distant from the root node. We suggest to evolve very large very complex programs, it will be necessary to adopt an open architecture where most mutation sites are within 10 to 100 levels of the organism's environment.
Cite
@article{arxiv.2112.00812,
title = {Evolving Open Complexity},
author = {W. B. Langdon},
journal= {arXiv preprint arXiv:2112.00812},
year = {2021}
}
Comments
Accepted for publication by SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation, ISSN 1931-8499, evolution.sigevo.org (publication expected 2022). 4 pages, 1 figure http://www.cs.ucl.ac.uk/staff/W.Langdon/gif/random_graph.svg.gz