research-article

The Effect of Fitness Function Design on Performance in Evolutionary Robotics: The Influence of a Priori Knowledge

Authors:

Mohammad Divband Soorati,

Heiko HamannAuthors Info & Claims

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 153 - 160

https://doi.org/10.1145/2739480.2754676

Published: 11 July 2015 Publication History

Abstract

Fitness function design is known to be a critical feature of the evolutionary-robotics approach. Potentially, the complexity of evolving a successful controller for a given task can be reduced by integrating a priori knowledge into the fitness function which complicates the comparability of studies in evolutionary robotics. Still, there are only few publications that study the actual effects of different fitness functions on the robot's performance. In this paper, we follow the fitness function classification of Nelson et al. (2009) and investigate a selection of four classes of fitness functions that require different degrees of a priori knowledge. The robot controllers are evolved in simulation using NEAT and we investigate different tasks including obstacle avoidance and (periodic) goal homing. The best evolved controllers were then post-evaluated by examining their potential for adaptation, determining their convergence rates, and using cross-comparisons based on the different fitness function classes. The results confirm that the integration of more a priori knowledge can simplify a task and show that more attention should be paid to fitness function classes when comparing different studies.

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Cited By

Nienaber SSoorati MGhasemzadeh AGhofrani J(2023)Software Product Lines for Development of Evolutionary RobotsProceedings of the 27th ACM International Systems and Software Product Line Conference - Volume B10.1145/3579028.3609018(77-84)Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1145/3579028.3609018
Medvet ENadizar GPigozzi FSalvato E(2023)Evolutionary Machine Learning in RoboticsHandbook of Evolutionary Machine Learning10.1007/978-981-99-3814-8_23(657-694)Online publication date: 2-Nov-2023
https://doi.org/10.1007/978-981-99-3814-8_23
Rosa LHomem VNolfi SCarvalho J(2022)On the Impact of the Duration of Evaluation Episodes on the Evolution of Adaptive RobotsParallel Problem Solving from Nature – PPSN XVII10.1007/978-3-031-14714-2_36(520-529)Online publication date: 14-Aug-2022
https://doi.org/10.1007/978-3-031-14714-2_36
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The Effect of Fitness Function Design on Performance in Evolutionary Robotics: The Influence of a Priori Knowledge
1. Computer systems organization
  1. Embedded and cyber-physical systems
2. Computing methodologies
  1. Artificial intelligence

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cover image ACM Conferences

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1496 pages

ISBN:9781450334723

DOI:10.1145/2739480

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Copyright © 2015 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 the author(s) 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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Published: 11 July 2015

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GECCO '15

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GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

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GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Nienaber SSoorati MGhasemzadeh AGhofrani J(2023)Software Product Lines for Development of Evolutionary RobotsProceedings of the 27th ACM International Systems and Software Product Line Conference - Volume B10.1145/3579028.3609018(77-84)Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1145/3579028.3609018
Medvet ENadizar GPigozzi FSalvato E(2023)Evolutionary Machine Learning in RoboticsHandbook of Evolutionary Machine Learning10.1007/978-981-99-3814-8_23(657-694)Online publication date: 2-Nov-2023
https://doi.org/10.1007/978-981-99-3814-8_23
Rosa LHomem VNolfi SCarvalho J(2022)On the Impact of the Duration of Evaluation Episodes on the Evolution of Adaptive RobotsParallel Problem Solving from Nature – PPSN XVII10.1007/978-3-031-14714-2_36(520-529)Online publication date: 14-Aug-2022
https://doi.org/10.1007/978-3-031-14714-2_36
Birattari MLigot AHasselmann K(2020)Disentangling automatic and semi-automatic approaches to the optimization-based design of control software for robot swarmsNature Machine Intelligence10.1038/s42256-020-0215-02:9(494-499)Online publication date: 10-Aug-2020
https://doi.org/10.1038/s42256-020-0215-0
Heinrich MSoorati MKaiser TWahby MHamann H(2019)Swarm robotics: Robustness, scalability, and self-X features in industrial applicationsit - Information Technology10.1515/itit-2019-000361:4(159-167)Online publication date: 30-Oct-2019
https://doi.org/10.1515/itit-2019-0003
Martins GUrbano PChristensen A(2018)Using Communication for the Evolution of Scalable Role Allocation in Collective RoboticsAdvances in Artificial Intelligence – IBERAMIA 201810.1007/978-3-030-03928-8_27(326-337)Online publication date: 2018
https://doi.org/10.1007/978-3-030-03928-8_27
Hofstadler DWahby MHeinrich MHamann HZahadat PAyres PSchmickl T(2017)Evolved Control of Natural PlantsACM Transactions on Autonomous and Adaptive Systems10.1145/312464312:3(1-24)Online publication date: 20-Sep-2017
https://dl.acm.org/doi/10.1145/3124643

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