- author = "Alexander Lalejini",
- title = "Evolving Phenotypically Plastic Digital Organisms",
- school = "Computer Science, Michigan State University",
- year = "2021",
- address = "USA",
- keywords = "genetic algorithms, genetic programming, Evolution (Biology)",
-
URL = "
https://d.lib.msu.edu/etd/49783",
-
URL = "https://d.lib.msu.edu/etd/49783/OBJ/download",
-
DOI = "
doi:10.25335/tk9p-ke12",
- size = "183 pages",
-
abstract = "The ability to dynamically respond to cues from the
environment is a fundamental feature of most adaptive
systems. In biological systems, changes to an organism
based on environmental cues is called phenotypic
plasticity. Indeed, phenotypic plasticity underlies
many of the adaptive traits and developmental patterns
found in nature and serves as a key mechanism for
responding to spatially or temporally variable
environments. Most computer programs require phenotypic
plasticity, as they must respond dynamically to stimuli
such as user input, sensor data, et cetera. As such,
phenotypic plasticity also has practical applications
in genetic programming, wherein we apply the natural
principles of evolution to automatically synthesize
computer programs rather than writing them by hand.
In this dissertation, I achieve two synergistic aims: (1) I use populations of self-replicating computer programs (digital organisms) to empirically study the conditions under which adaptive phenotypic plasticity evolves and how its evolution shapes subsequent evolutionary outcomes; and (2) I transfer insights from biology to develop novel genetic programming techniques in order to evolve more responsive (i.e., phenotypically plastic) computer programs. First, I illustrate the importance of mutation rate, environmental change, and partially-plastic building blocks for the evolution of adaptive plasticity. Next, I show that adaptive phenotypic plasticity stabilizes populations against environmental change, allowing them to more easily retain novel adaptive traits. Finally, I improve our ability to evolve phenotypically plastic computer programs with three novel genetic programming techniques: (1) SignalGP, which provides mechanisms to control code expression based on environmental cues, (2) tag-based genetic regulation to adjust code expression based on current context, and (3) tag-accessed memory to provide more dynamic mechanisms for storing data.",
-
notes = "Ecology, Evolutionary Biology, and Behavior - Dual
Major
Supervisor: Charles Ofria",
