keywords = "genetic algorithms, genetic programming, Parallel
Evolutionary Algorithms, Software Tools, Open BEAGLE,
DEAP, Distributed Evolutionary Algorithms in Python",
isbn13 = "978-1-4503-1178-6",
DOI = "doi:10.1145/2330784.2330799",
code_url = "https://github.com/deap",
size = "8 pages",
abstract = "DEAP (Distributed Evolutionary Algorithms in Python)
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
type of frameworks. It also incorporates easy
parallelism where users need not concern themselves
with gory implementation details like synchronisation
and load balancing, only functional decomposition.
Several examples illustrate the multiple properties of
DEAP.",
notes = "Also known as \cite{2330799} Distributed at
GECCO-2012.