Evolving dynamic fitness measures for genetic programming
| Autor: | Nelishia Pillay, Anisa W. Ragalo |
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| Rok vydání: | 2018 |
| Předmět: |
Sequence
Computer science business.industry General Engineering Genetic programming 0102 computer and information sciences 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Computer Science Applications 010201 computation theory & mathematics Artificial Intelligence Genetic algorithm 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Expert Systems with Applications. 109:162-187 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2018.03.060 |
| Popis: | This research builds on the hypothesis that the use of different fitness measures on the different generations of genetic programming (GP) is more effective than the convention of applying the same fitness measure individually throughout GP. Whereas the previous study used a genetic algorithm (GA) to induce the sequence in which fitness measures should be applied over the GP generations, this research uses a meta- (or high-level) GP to evolve a combination of the fitness measures for the low-level GP. The study finds that the meta-GP is the preferred approach to generating dynamic fitness measures. GP systems applying the generated dynamic fitness measures consistently outperform the previous approach, as well as standard GP on benchmark and real world problems. Furthermore, the generated dynamic fitness measures are shown to be reusable, whereby they can be used to solve unseen problems to optimality. |
| Databáze: | OpenAIRE |
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