https://github.com/amlalejini/gp-tag-accessed-memory
Explorations of tag-accessed memory for genetic programming
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Explorations of tag-accessed memory for genetic programming
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https://github.com/amlalejini/gp-tag-accessed-memory/blob/master/
# Tag-accessed Memory for Genetic Programming **Navigation** - [Project Overview](#project-overview) - [Tag-accessed Memory](#tag-accessed-memory) - [References](#references) ## Project Overview This project explores the efficacy of tag-accessed memory. ### Tag-accessed Memory Tags are evolvable labels that give genetic programs a flexible mechanism for specification. Tag-based naming schemes have been demonstrated for labeling and referencing program modules (Spector, 2011; Lalejini and Ofria, 2018). We continue to expand the use of tags in GP by incorporating tag-based referencing into the memory model of a simple linear GP representation. In this study, memory comprises 16 statically tagged memory registers, and instructions use tag-based referencing to refer to positions in memory. Programs our simple representation are linear sequences of instructions, and each instruction has three tag-based arguments, which may modify the instruction's behavior. Below, we provide a visual example, contrasting traditional direct-indexed memory access and tag-based memory access.  In the example above, both programs have identical behavior: requesting input, setting the second register to the terminal value '2', multiplying the input by 2, and outputting the result. ## References Lalejini, A., & Ofria, C. (2018). Evolving event-driven programs with SignalGP. In Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO 18 (pp. 11351142). New York, New York, USA: ACM Press. https://doi.org/10.1145/3205455.3205523 R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Spector, L., Martin, B., Harrington, K., & Helmuth, T. (2011). Tag-based modules in genetic programming. In Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO 11 (p. 1419). New York, New York, USA: ACM Press. https://doi.org/10.1145/2001576.2001767
Owner
- Name: Alex Lalejini
- Login: amlalejini
- Kind: user
- Location: Grand Rapids, MI
- Company: Grand Valley State University
- Website: https://lalejini.com
- Twitter: amlalejini
- Repositories: 98
- Profile: https://github.com/amlalejini
Assistant Professor @ Grand Valley State University
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