Abstract
For both practical reasons and those of habit, most evolutionary computation research is presented in highly summary form. These summaries, however, often obscure or completely mask the profusion of specific selections, crossovers, and mutations that are ultimately responsible for the aggregate behaviors we’re interested in. In this chapter we take a different approach and use the Neo4j graph database system to record and analyze the entire genealogical history of a set of genetic programming runs. We then explore a few of these runs in detail, discovering important properties of lexicase selection; these may in turn help us better understand the dynamics of lexicase selection, and the ways in which it differs from tournament selection. More broadly, we illustrate the value of recording and analyzing this level of detail, both as a means of understanding the dynamics of particular runs, and as a way of generating questions and ideas for subsequent, broader study.
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Acknowledgements
Thanks to the members of the Hampshire College Computational Intelligence Lab and M. Kirbie Dramdahl at the University of Minnesota, Morris, for discussions that helped to improve the work described in this chapter. Thanks also to Josiah Erikson for systems support, and to Hampshire College for support for the Hampshire College Institute for Computational Intelligence. This material is based upon work supported by the National Science Foundation under Grants No. 1017817, 1129139, and 1331283. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation.
We are very grateful to all the participants in the 2015 Genetic Programming Theory and Practice (GPTP) workshop for their enthusiasm, ideas, and support. In particular we’d like to thank William Tozier for all manner of suggestions and feedback, and in particular for helping us understand the connection between our work and the Pickering’s idea of the “mangle of practice”. Krzysztof Krawiec provided a number of valuable suggestions based on an early draft. Steven Gustafson suggested that we look into SPARQL and triplestore databases as an alternative to Neo4j, an interesting idea we haven’t had time to explore in detail. Stuart Card connected us to the interesting related work by Karthik Kuber. Finally, thanks to the GPTP organizers; without their hard work none of those other valuable conversations would have occurred.
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McPhee, N.F., Donatucci, D., Helmuth, T. (2016). Using Graph Databases to Explore the Dynamics of Genetic Programming Runs. In: Riolo, R., Worzel, W., Kotanchek, M., Kordon, A. (eds) Genetic Programming Theory and Practice XIII. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-34223-8_11
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