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DEAP: a python framework for evolutionary algorithms

Published: 07 July 2012 Publication History

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.

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cover image ACM Conferences
GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
July 2012
1586 pages
ISBN:9781450311786
DOI:10.1145/2330784
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 07 July 2012

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Author Tags

  1. parallel evolutionary algorithms
  2. software tools

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GECCO '12
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GECCO '12: Genetic and Evolutionary Computation Conference
July 7 - 11, 2012
Pennsylvania, Philadelphia, USA

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  • (2024)Tightening the Approximation Error of Adversarial Risk with Auto Loss Function SearchProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664104(1563-1572)Online publication date: 14-Jul-2024
  • (2024)Final Productive Fitness for Surrogates in Evolutionary AlgorithmsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654433(583-586)Online publication date: 14-Jul-2024
  • (2024)Using Bayesian Optimization to Improve Hyperparameter Search in TPOTProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654061(340-348)Online publication date: 14-Jul-2024
  • (2024)Genetic Programming and Reinforcement Learning on Learning Heuristics for Dynamic Scheduling: A Preliminary ComparisonIEEE Computational Intelligence Magazine10.1109/MCI.2024.336397019:2(18-33)Online publication date: May-2024
  • (2024)Learning Heuristics via Genetic Programming for Multi-Mode Resource-Constrained Project Scheduling2024 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC60901.2024.10612172(01-08)Online publication date: 30-Jun-2024
  • (2024)Novel Genotypic Diversity Metrics for Real-Coded Optimization on Multi-Modal Problems2024 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC60901.2024.10611897(1-8)Online publication date: 30-Jun-2024
  • (2024)The Role of Random Walk-Based Techniques in Enhancing Metaheuristic Optimization Algorithms—A Systematic and Comprehensive ReviewIEEE Access10.1109/ACCESS.2024.346617012(139573-139608)Online publication date: 2024
  • (2024)A Novel Throughput Enhancement Method for Deep Learning Applications on Mobile Devices With Heterogeneous ProcessorsIEEE Access10.1109/ACCESS.2024.337551712(38773-38785)Online publication date: 2024
  • (2024)Classification of bioactive peptides: A systematic benchmark of models and encodingsComputational and Structural Biotechnology Journal10.1016/j.csbj.2024.05.04023(2442-2452)Online publication date: Dec-2024
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