Patterns for evolving frameworks
Pattern languages of program design 3
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Evolving Objects: A General Purpose Evolutionary Computation Library
Selected Papers from the 5th European Conference on Artificial Evolution
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Analysis of a master-slave architecture for distributed evolutionary computations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Pareto-efficient hybridization for multi-objective recommender systems
Proceedings of the sixth ACM conference on Recommender systems
DEAP: enabling nimbler evolutions
ACM SIGEVOlution
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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.