research-article

MABE 2.0: an introduction to MABE and a road map for the future of MABE development

Authors:

Alexander Lalejini,

Charles OfriaAuthors Info & Claims

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1349 - 1356

https://doi.org/10.1145/3319619.3326825

Published: 13 July 2019 Publication History

Abstract

MABE (Modular Agent-based Evolver) is an open-source evolutionary computation (EC) research platform designed to be used by biologists, engineers, computer scientists, and other researchers. MABE's primary goal is to reduce the time between thinking up a new hypothesis and generating results. The design assumes that there are common elements in many EC research projects. MABE improves efficiency by allowing for the reuse of these common elements and standardizing of interfaces for non-common elements so that they can be used interchangeably. As of the writing of this paper, the MABE framework is five years old. Here, we reflect on the current version of MABE, including its successes and shortcomings, and propose upgrades for the next release.

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Cited By

Lundbak Olesen CWaade PAlbantakis LMathys C(2023)Phi fluctuates with surprisal: An empirical pre-study for the synthesis of the free energy principle and integrated information theoryPLOS Computational Biology10.1371/journal.pcbi.101134619:10(e1011346)Online publication date: 20-Oct-2023
https://doi.org/10.1371/journal.pcbi.1011346
Seals DWilkes RTauritz DSilva SPaquete L(2023)Maelstrom: An Accelerator-compatible GP FrameworkProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596359(1882-1890)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3596359
Moreno MDolson EOfria C(2022)hstrat: a Python Package for phylogenetic inference on distributed digital evolution populationsJournal of Open Source Software10.21105/joss.048667:80(4866)Online publication date: Dec-2022
https://doi.org/10.21105/joss.04866
Show More Cited By

Index Terms

MABE 2.0: an introduction to MABE and a road map for the future of MABE development
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches
        Artificial life
2. Software and its engineering
  1. Software creation and management
    1. Designing software
      1. Software implementation planning
    2. Software post-development issues
  2. Software notations and tools
    1. Development frameworks and environments

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cover image ACM Conferences

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2019

2161 pages

ISBN:9781450367486

DOI:10.1145/3319619

Editor:
Manuel López-Ibáñez
University of Manchester, UK
,
General Chairs:
Anne Auger
Inria and Ecole Polytechnique, France
,
Thomas Stützle
IRIDIA, Université libre de Bruxelles Belgium

Copyright © 2019 ACM.

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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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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New York, NY, United States

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Published: 13 July 2019

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GECCO '19: Genetic and Evolutionary Computation Conference

July 13 - 17, 2019

Prague, Czech Republic

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Cited By

Lundbak Olesen CWaade PAlbantakis LMathys C(2023)Phi fluctuates with surprisal: An empirical pre-study for the synthesis of the free energy principle and integrated information theoryPLOS Computational Biology10.1371/journal.pcbi.101134619:10(e1011346)Online publication date: 20-Oct-2023
https://doi.org/10.1371/journal.pcbi.1011346
Seals DWilkes RTauritz DSilva SPaquete L(2023)Maelstrom: An Accelerator-compatible GP FrameworkProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596359(1882-1890)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3596359
Moreno MDolson EOfria C(2022)hstrat: a Python Package for phylogenetic inference on distributed digital evolution populationsJournal of Open Source Software10.21105/joss.048667:80(4866)Online publication date: Dec-2022
https://doi.org/10.21105/joss.04866
Shahbandegan SHernandez JLalejini ADolson EWagner M(2022)Untangling phylogenetic diversity's role in evolutionary computation using a suite of diagnostic fitness landscapesProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3534028(2322-2325)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3534028

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