2012
JMLR
JMLR 2012
DEAP: Evolutionary Algorithms Made Easy
Abstract
DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. Its design departs from most other existing frameworks in that it seeks to make algorithms explicit and data structures transparent, as opposed to the more common black-box frameworks. Freely available with extensive documentation at http://deap.gel.ulaval.ca, DEAP is an open source project under an LGPL license. [abs] [ pdf ][ bib ] [ code ] © JMLR 2012. (edit, beta)
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Interdisciplinary Bridge
— Computer Science and Mathematics & Optimization
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Keyword Pioneer
— fitness function
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Hot Topic Early Bird
— genetic algorithm
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio