Abstract
We introduce a parallel version of hierarchical evolutionary re-combination (herc) and use it to evolve programs for ten standard string processing tasks and a postfix calculator emulation task. Each processor maintains a separate evolutionary niche, with its own ladder of competing agents and codebank of potential mates. Further enhancements include evolution of multi-cell programs and incremental learning with reshuffling of data. We find the success rate is improved by transgenic evolution, where solutions to earlier tasks are recombined to solve later tasks. Sharing of genetic material between niches seems to improve performance for the postfix task, but for some of the string processing tasks it can increase the risk of premature convergence.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Blair, A.: Learning the Caesar and Vigenere Cipher by hierarchical evolutionary re-combination. In: Congress on Evolutionary Computation, pp. 605–612 (2013)
Blair, A.: Incremental evolution of hercl programs for robust control. In: Conference on Genetic and Evolutionary Computation Companion, pp. 27–28 (2014)
Blair, A.D.: Transgenic evolution for classification tasks with HERCL. In: Chalup, S.K., Blair, A.D., Randall, M. (eds.) ACALCI 2015. LNCS, vol. 8955, pp. 185–195. Springer, Heidelberg (2015)
Bruce, W.S.: The lawnmower problem revisited: stack-based genetic programming and automatically defined functions. In: Conference on Genetic Programming, pp. 52–57 (1997)
Helmuth, T., Spector, L.: General program synthesis benchmark suite. In: Genetic and Evolutionary Computation Conference, pp. 1039–1046 (2015)
Hornby, G.S.: ALPS: the age-layered population structure for reducing the problem of premature convergence. In: GECCO, pp. 815–822 (2006)
Lehman, J., Stanley, K.O.: Abandoning objectives: evolution through the search for novelty alone. Evol. Comput. 19(2), 198–223 (2011)
Nordin, P.: A compiling genetic programming system that directly manipulates the machine code. Adv. Genet. Program 1, 311–331 (1994)
Perkis, T.: Stack-based genetic programming. In: IEEE World Congress on Computational Intelligence, pp. 148–153 (1994)
Spector, L., Robinson, A.: Genetic programming and autoconstructive evolution with the push programming language. Genet. Program Evolvable Mach. 3(1), 7–40 (2002)
Acknowledgment
This research was undertaken using computing resources from Intersect Australia’s Orange HPC, provided through the National Computational Infrastructure (NCI), which is supported by the Australian Government.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Soderlund, J., Vickers, D., Blair, A. (2016). Parallel Hierarchical Evolution of String Library Functions. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham. https://doi.org/10.1007/978-3-319-45823-6_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-45823-6_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45822-9
Online ISBN: 978-3-319-45823-6
eBook Packages: Computer ScienceComputer Science (R0)