Name
Abstract | ||
|---|---|---|
DEAP (Distributed Evolutionary Algorithms in Python) is a novel volutionary 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 synchronization and load balancing, only functional decomposition. Several examples illustrate the multiple properties of DEAP. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2330784.2330799 | GECCO (Companion) |
Keywords | Field | DocType |
evolutionary algorithm,existing framework,easy parallelism,functional decomposition,data structure,evolutionary algorithms,gory implementation detail,common black box type,design departs,multiple property,python framework,load balancing,load balance | Black box (phreaking),Data structure,Evolutionary algorithm,Computer science,Load balancing (computing),Functional decomposition,Theoretical computer science,DEAP,Evolutionary music,Python (programming language) | Conference |
Citations | PageRank | References |
13 | 0.95 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| François-Michel De Rainville | 1 | 199 | 9.27 |
| Félix-Antoine Fortin | 2 | 230 | 9.07 |
| Marc-André Gardner | 3 | 222 | 11.20 |
| Marc Parizeau | 4 | 811 | 87.35 |
| Christian Gagné | 5 | 627 | 52.38 |