Novelty Search for Shape Descriptors
| Autor: | Roisin McConnell, Simon Hickinbotham, Andy M. Tyrrell, Wei Zhang, Imelda Friel, Mark Price |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Fitness function
business.industry 0211 other engineering and technologies Novelty Evolutionary algorithm 02 engineering and technology Random walk Machine learning computer.software_genre Range (mathematics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Genotype to phenotype Generative Design business Design space computer 021106 design practice & management |
| Zdroj: | CEC |
| DOI: | 10.1109/cec48606.2020.9185745 |
| Popis: | In nature, the exploration of a design space is achieved by evolution. However, using artificial evolution to evolve physical shapes has been challenging because both the mapping from the genotype to phenotype and the means of measuring the resulting shape to estimate fitness are not straightforward. This contribution brings together recent advances in generative design with novelty search, an evolutionary method that replaces the fitness function with reward based purely on novelty. Bringing these techniques together yields a new methodology for exploring the power of shape descriptors. Novelty search with and without an archive is used to explore the range of shapes that are reachable from a hand-designed genotype, and compared with random walk mutations. Results indicate that the novelty search technique without an archive evolves a wider range of shapes than when an archive is used, but both are better than random walk. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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