Learning With Case-Injected Genetic Algorithms
| Autor: | John R. McDonnell, Sushil J. Louis |
|---|---|
| Rok vydání: | 2004 |
| Předmět: |
Mathematical optimization
education.field_of_study Optimization problem business.industry Computer science media_common.quotation_subject Population Specific knowledge Theoretical Computer Science Computational Theory and Mathematics Search algorithm Genetic algorithm Case-based reasoning Quality (business) Artificial intelligence Set (psychology) business education Software media_common |
| Zdroj: | IEEE Transactions on Evolutionary Computation. 8:316-328 |
| ISSN: | 1089-778X |
| Popis: | This paper presents a new approach to acquiring and using problem specific knowledge during a genetic algorithm (GA) search. A GA augmented with a case-based memory of past problem solving attempts learns to obtain better performance over time on sets of similar problems. Rather than starting anew on each problem, we periodically inject a GA's population with appropriate intermediate solutions to similar previously solved problems. Perhaps, counterintuitively, simply injecting solutions to previously solved problems does not produce very good results. We provide a framework for evaluating this GA-based machine-learning system and show experimental results on a set of design and optimization problems. These results demonstrate the performance gains from our approach and indicate that our system learns to take less time to provide quality solutions to a new problem as it gains experience from solving other similar problems in design and optimization. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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