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dc.contributor.authorBASHER, SHEIKH FAISHAL
dc.date.accessioned2021-11-11T16:06:25Z
dc.date.available2021-11-11T16:06:25Z
dc.identifier.urihttp://hdl.handle.net/10464/15388
dc.description.abstractThis thesis proposes a new approach to evolving a diversity of high-quality solutions for problems having many objectives. Mouret and Clune's MAP-Elites algorithm has been proposed as a way to evolve an assortment of diverse solutions to a problem. We extend MAP-Elites in a number of ways. Firstly, we introduce a many-objective strategy called sum-of-ranks, which enables problems with many objectives (4 and more) to be considered in the MAP. Secondly, we enhance MAP-Elites by extending it with multiple solutions per "grid" cell (the original MAP-Elites saves only a single solution per cell). A few different ways of selecting cell members for reproduction are also considered. We test the new MAP-Elites strategies on the evolutionary art application of image generation. Using procedural textures, genetic programming is used with upwards of 15 lightweight image features to guide fitness. The goal is to evolve images that share image features with a given target image. Our experiments show that the new MAP-Elites algorithms produce a large number of diverse solutions of varying quality. The extended MAP-Elites algorithm is also statistically competitive compared to vanilla GP in this application domain.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectMAP-Elitesen_US
dc.subjectMany-objective Optimizationen_US
dc.subjectSum-of-ranksen_US
dc.subjectProcedural Textureen_US
dc.subjectGenetic Programmingen_US
dc.titleManaging Diversity and Many Objectives in Evolutionary Designen_US
dc.typeElectronic Thesis or Dissertationen_US
dc.degree.nameM.Sc. Computer Scienceen_US
dc.degree.levelMastersen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.degree.disciplineFaculty of Mathematics and Scienceen_US
refterms.dateFOA2021-11-11T16:06:25Z


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