Spatial Genetic Programming
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| Izdano u: | ProQuest Dissertations and Theses (2024) |
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ProQuest Dissertations & Theses
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| 045 | 2 | |b d20240101 |b d20241231 | |
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| 100 | 1 | |a Miralavy, Iliya | |
| 245 | 1 | |a Spatial Genetic Programming | |
| 260 | |b ProQuest Dissertations & Theses |c 2024 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a Space, while inherent to the natural world, often finds itself omitted in bio-inspired computational system designs. Spatial Genetic Programming (SGP) is a Genetic Programming (GP) paradigm that incorporates space as a fundamental dimension, evolving alongside Linear Genetic Programming (LGP) programs. In SGP, each individual model is represented by a 2D space consisting of one or many LGP programs. These programs execute in an order controlled by their spatial position. The contributions of this work are: Introducing SGP as a tool for studying evolution of space in GP. Application of the proposed system to a range of problems including symbolic regression, classic control and decision-making problems and a comparison to other common GP paradigms. A study on how spatial dimension influences generational diversity, on emergence of spatially-induced localization within the system, and on the emergence of iterative structures within the system. The findings of this research open new avenues towards a better understanding of natural evolution and how the dimension of space could be useful as a handle for controlling important aspects of evolution. | |
| 653 | |a Computer science | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Genetics | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2024) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2917123274/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2917123274/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |