Software review: DEAP (Distributed Evolutionary Algorithm in Python) library

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@Article{Kim:2019:GPEM,

• author = "Jinhan Kim and Shin Yoo",
• title = "Software review: {DEAP} (Distributed Evolutionary Algorithm in Python) library",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2019",
• volume = "20",
• number = "1",
• pages = "139--142",
• month = mar,
• note = "Software Review",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-018-9341-4",
• size = "4 pages",
• abstract = "We give a critical assessment of the DEAP (Distributed Evolutionary Algorithm in Python) open-source library and highly recommend it to both beginners and experts alike. DEAP supports a range of evolutionary algorithms including both strongly and loosely typed Genetic Programming, Genetic Algorithm, and Multi-Objective Evolutionary Algorithms such as NSGA-II and SPEA2. It contains most of the basic functions required by evolutionary computation, so that its users can easily construct various flavours of both single and multi-objective evolutionary algorithms and execute them using multiple processors. It is ideal for fast prototyping and can be used with an abundance of other Python libraries for data processing as well as other machine learning techniques.",

}

Genetic Programming entries for Jinhan Kim Shin Yoo

Citations