Library for Evolutionary Algorithms in Python (LEAP)

Mark Coletti; Eric Scott; Jeff Bassett

Library for Evolutionary Algorithms in Python (LEAP)

Mark Coletti (Developer), Eric Scott (Developer), Jeff Bassett (Developer)

Learning Systems

Research output: Non-textual form › Software

Abstract

LEAP is a general purpose Evolutionary Computation package that combines readable and easy-to-use syntax for search and optimization algorithms with powerful distribution and visualization features.

LEAP's signature is its operator pipeline, which uses a simple list of functional operators to concisely express a metaheuristic algorithm's configuration as high-level code. Adding metrics, visualization, or special features (like distribution, coevolution, or island migrations) is often as simple as adding operators into the pipeline.

Original language	American English
Media of output	Online
State	Published - 2020

Access to Document

https://github.com/AureumChaos/LEAPLicense: Other

1 Conference contribution

Library for evolutionary algorithms in Python (LEAP)
Coletti, M. A., Scott, E. O. & Bassett, J. K., Jul 8 2020, GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, p. 1571-1579 9 p. (GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Open Access
26 Scopus citations

Cite this

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title = "Library for Evolutionary Algorithms in Python (LEAP)",

abstract = "LEAP is a general purpose Evolutionary Computation package that combines readable and easy-to-use syntax for search and optimization algorithms with powerful distribution and visualization features.LEAP's signature is its operator pipeline, which uses a simple list of functional operators to concisely express a metaheuristic algorithm's configuration as high-level code. Adding metrics, visualization, or special features (like distribution, coevolution, or island migrations) is often as simple as adding operators into the pipeline.",

author = "Mark Coletti and Eric Scott and Jeff Bassett",

year = "2020",

language = "American English",

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T1 - Library for Evolutionary Algorithms in Python (LEAP)

A2 - Coletti, Mark

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A2 - Bassett, Jeff

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N2 - LEAP is a general purpose Evolutionary Computation package that combines readable and easy-to-use syntax for search and optimization algorithms with powerful distribution and visualization features.LEAP's signature is its operator pipeline, which uses a simple list of functional operators to concisely express a metaheuristic algorithm's configuration as high-level code. Adding metrics, visualization, or special features (like distribution, coevolution, or island migrations) is often as simple as adding operators into the pipeline.

AB - LEAP is a general purpose Evolutionary Computation package that combines readable and easy-to-use syntax for search and optimization algorithms with powerful distribution and visualization features.LEAP's signature is its operator pipeline, which uses a simple list of functional operators to concisely express a metaheuristic algorithm's configuration as high-level code. Adding metrics, visualization, or special features (like distribution, coevolution, or island migrations) is often as simple as adding operators into the pipeline.

M3 - Software

ER -

Library for Evolutionary Algorithms in Python (LEAP)

Abstract

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Research output

Library for evolutionary algorithms in Python (LEAP)

Cite this