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Lineage Selection in Mixed Populations for Genetic Improvement
Abstract: 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.
Status: Published. You can share this link.
Venue: ALife 2022.
Requirements: Dana v254 download
Download: alife2022rainford.zip