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Penny Faulkner Rainford
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Proceedings Papers
. isal2022, ALIFE 2022: The 2022 Conference on Artificial Life16, (July 18–22, 2022) 10.1162/isal_a_00494
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Emergent Software Systems take a large pool of potential building blocks, for a given system such as a web server, and learn at runtime how best to compose selected blocks from that pool in order to maximise some utility function in each set of deployment conditions that is encountered. To support this approach, at least some building blocks in the available pool must have implementation variants – alternatives which have the same functionality but achieve it using a different approach (such as different sorting algorithms or different cache eviction policies). We can automatically derive new building block variants for our pool of potential behaviour by using genetic improvement (GI), which has long proven effective for optimisation and repair of source code. When a novel deployment environment is detected, however, it is unclear which existing building block variant(s) should be used as starting points for new a GI process to tailor a new block for that environment; in this situation it would be necessary to try one GI process from every possible existing building block variant as a starting point, a process which could be extremely expensive. In this paper we present a mixed-population approach to examine whether GI can simultaneously offer both lineage selection and optimisation to find the ideal source code for a new building block variant tailored to a given environment. Using a lowest-common-ancestor approach to producing evolvable individuals, our results demonstrate strong evidence that combined lineage selection and optimisation is viable in multiple scenarios, offering far reduced compute time to locate a good individual for a novel environment.
Proceedings Papers
. isal2020, ALIFE 2020: The 2020 Conference on Artificial Life104-112, (July 13–18, 2020) 10.1162/isal_a_00270
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We present a new modelling approach for complex systems incorporating a dynamic environment and individuals with agency. We do this through multiple models at different levels. We develop a common meta-model for these kinds of models. The meta-model captures the concepts of agents moving and interacting on a dynamic network, to provide the power of an agent based model situated in the context of a dynamic and changing environment. The addition of context allows us to isolate the decision process of the agent from the constraints and resources provided by the environment, so we can consider separately the effect of changes in the environment from changes in the agents’ decision process, and changes caused by agents acting differently based on their learning from, and adapting to, the changed environment. We develop a generalised platform model for implementing different complex systems conforming to the meta-model. We illustrate the approach by developing a domain model for a particular system of interest, a simplified model of declining mobility, which we use to guide the specialisation of the generic platform model to an implementation and to perform simulation experiments.