| Autor: | Ryszard S. Michalski |
|---|---|
| Rok vydání: | 2000 |
| Předmět: |
education.field_of_study
Class (computer programming) Fitness function business.industry Computer science Population Machine learning computer.software_genre Evolutionary computation Range (mathematics) Artificial Intelligence Artificial intelligence Automatic programming business education Evolvable hardware Learnable Evolution Model computer Software |
| Zdroj: | Machine Learning. 38:9-40 |
| ISSN: | 0885-6125 |
| DOI: | 10.1023/a:1007677805582 |
| Popis: | A new class of evolutionary computation processes is presented, called Learnable Evolution Model or LEM. In contrast to Darwinian-type evolution that relies on mutation, recombination, and selection operators, LEM employs machine learning to generate new populations. Specifically, in Machine Learning mode, a learning system seeks reasons why certain individuals in a population (or a collection of past populations) are superior to others in performing a designated class of tasks. These reasons, expressed as inductive hypotheses, are used to generate new populations. A remarkable property of LEM is that it is capable of quantum leaps (“insight jumps”) of the fitness function, unlike Darwinian-type evolution that typically proceeds through numerous slight improvements. In our early experimental studies, LEM significantly outperformed evolutionary computation methods used in the experiments, sometimes achieving speed-ups of two or more orders of magnitude in terms of the number of evolutionary steps. LEM has a potential for a wide range of applications, in particular, in such domains as complex optimization or search problems, engineering design, drug design, evolvable hardware, software engineering, economics, data mining, and automatic programming. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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