AutoMoDe-Pomodoro: an evolutionary class of modular designs

Nicolas Cambier; Eliseo Ferrante

doi:10.1145/3520304.3529031

AutoMoDe-Pomodoro: an evolutionary class of modular designs

Nicolas Cambier
, Eliseo Ferrante

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

In this paper, we reintroduce evolutionary algorithms into Auto-MoDe, an automatic design approach which optimizes behavioural modules into a probabilistic finite automaton. We evaluate three approaches, with different encodings of the probabilistic finite automaton phenotype, and observe their performances. This work opens modular designs to more advanced evolutionary robotics methods, such as novelty search and embodied evolution.

Original language	English
Title of host publication	GECCO 2022
Subtitle of host publication	Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion
Publisher	Association for Computing Machinery, Inc
Pages	100-103
Number of pages	4
ISBN (Electronic)	9781450392686
DOIs	https://doi.org/10.1145/3520304.3529031
Publication status	Published - Jul 2022
Event	2022 Genetic and Evolutionary Computation Conference, GECCO 2022 - Virtual, Online, United States Duration: 9 Jul 2022 → 13 Jul 2022

Conference

Conference	2022 Genetic and Evolutionary Computation Conference, GECCO 2022
Country/Territory	United States
City	Virtual, Online
Period	9/07/22 → 13/07/22

Bibliographical note

Publisher Copyright:
© 2022 Owner/Author.

Keywords

evolutionary robotics
genetic algorithm
modular design
swarm robotics

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10.1145/3520304.3529031

AutoMoDe-pomodoroFinal published version, 551 KBLicence: Article 25fa Dutch Copyright Act

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title = "AutoMoDe-Pomodoro: an evolutionary class of modular designs",

abstract = "In this paper, we reintroduce evolutionary algorithms into Auto-MoDe, an automatic design approach which optimizes behavioural modules into a probabilistic finite automaton. We evaluate three approaches, with different encodings of the probabilistic finite automaton phenotype, and observe their performances. This work opens modular designs to more advanced evolutionary robotics methods, such as novelty search and embodied evolution.",

keywords = "evolutionary robotics, genetic algorithm, modular design, swarm robotics",

author = "Nicolas Cambier and Eliseo Ferrante",

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year = "2022",

month = jul,

doi = "10.1145/3520304.3529031",

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Cambier, N & Ferrante, E 2022, AutoMoDe-Pomodoro: an evolutionary class of modular designs. in GECCO 2022: Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, pp. 100-103, 2022 Genetic and Evolutionary Computation Conference, GECCO 2022, Virtual, Online, United States, 9/07/22. https://doi.org/10.1145/3520304.3529031

AutoMoDe-Pomodoro: an evolutionary class of modular designs. / Cambier, Nicolas; Ferrante, Eliseo.
GECCO 2022: Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, 2022. p. 100-103.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AU - Ferrante, Eliseo

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KW - evolutionary robotics

KW - genetic algorithm

KW - modular design

KW - swarm robotics

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DO - 10.1145/3520304.3529031

M3 - Conference contribution

AN - SCOPUS:85136334956

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BT - GECCO 2022

PB - Association for Computing Machinery, Inc

T2 - 2022 Genetic and Evolutionary Computation Conference, GECCO 2022

Y2 - 9 July 2022 through 13 July 2022

ER -

AutoMoDe-Pomodoro: an evolutionary class of modular designs

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