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A. E. Eiben - IEEE Xplore Author Profile
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A. E. Eiben

Also published under: Agoston E. Eiben

Publications
70
Citations
2,560
Publications by Year
19942025
Co-Authors:
Show All Co-Authors (108)
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A. E. Eiben

Also published under: Agoston E. Eiben

Affiliation

Department of Computer Science
Vrije Universiteit Amsterdam
Amsterdam, Netherlands

Publication Topics

Biography

A. E. Eiben received the M.Sc. degree from ELTE University, Budapest, Hungary, and the Ph.D. degree from Eindhoven University of Technology, Eindhoven, The Netherlands.,Since 1999, he has been a full-time Professor with VU University Amsterdam, Amsterdam, The Netherlands. He was one of the founding fathers of EvoNet, the European EC Network that initiated the EuroGP and EvoStar conferences. He has been an Editorial Board Member of several EC journals and has co-organized various EC conferences. He has co-authored Introduction to Evolutionary Computing and has recently become active in evolutionary robotics.(Based on document published on 25 February 2014).
Publications
70
Citations
2,560
Publications by Year
19942025
Co-Authors:
Show All Co-Authors (108)
Author's Published Works

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Active Robot Curriculum Learning from Online Human Demonstrations

Muhan Hou;Koen Hindriks;A.E. Eiben;Kim Baraka

2025 20th ACM/IEEE International Conference on Human-Robot Interaction (HRI)
Year: 2025 | Conference Paper |
Learning from Demonstrations (LfD) allows robots to learn skills from human users, but its effectiveness can suffer due to sub-optimal teaching, especially from untrained demonstrators. Active LfD aims to improve this by letting robots actively request demonstrations to enhance learning. However, this may lead to frequent context switches between various task situations, increasing the human cogni...Show More

Active Robot Curriculum Learning from Online Human Demonstrations

Muhan Hou;Koen Hindriks;A.E. Eiben;Kim Baraka

Year: 2025 | Conference Paper |

Comparing Robot Controller Optimization Methods on Evolvable Morphologies

Fuda van Diggelen;Eliseo Ferrante;A. E. Eiben

Evolutionary Computation
Year: 2024 | Volume: 32, Issue: 2 | Journal Article |
In this paper, we compare Bayesian Optimization, Differential Evolution, and an Evolution Strategy employed as a gait-learning algorithm in modular robots. The motivational scenario is the joint evolution of morphologies and controllers, where “newborn” robots also undergo a learning process to optimize their inherited controllers (without changing their bodies). This context raises the question: ...Show More

Comparing Robot Controller Optimization Methods on Evolvable Morphologies

Fuda van Diggelen;Eliseo Ferrante;A. E. Eiben

Year: 2024 | Volume: 32, Issue: 2 | Journal Article |

Gait-Learning with Morphologically Evolving Robots Generated by L-System

Jie Luo;Daan Zeeuwe;Agoston E. Eiben

2024 International Conference on Electrical, Computer and Energy Technologies (ICECET
Year: 2024 | Conference Paper |
When controllers (brains) and morphologies (bodies) of robots simultaneously evolve, this can lead to a problem, namely the brain & body mismatch problem. In this research, we propose a solution of lifetime learning. We set up a system where modular robots can create offspring that inherit the bodies of parents by recombination and mutation. With regards to the brains of the offspring, we use two ...Show More

Gait-Learning with Morphologically Evolving Robots Generated by L-System

Jie Luo;Daan Zeeuwe;Agoston E. Eiben

Year: 2024 | Conference Paper |

An Approach to Representation Learning in Morphological Robot Evolution

Aart C. Stuurman;Anil Yaman;A.E. Eiben

2023 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2023 | Conference Paper |
A key challenge for evolving complex physical ob-jects is to design a representation, that is, to devise suitable genotypes and a good mapping from genotypes to phenotypes (the objects to be evolved). This paper outlines a new approach to address this challenge for evolving robot morphologies and presents a proof-of-concept study to assess its feasibility. The key idea is to design genotype-phenot...Show More

An Approach to Representation Learning in Morphological Robot Evolution

Aart C. Stuurman;Anil Yaman;A.E. Eiben

Year: 2023 | Conference Paper |

Morphological-Novelty in Modular Robot Evolution

Oliver Weissl;A.E. Eiben

2023 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2023 | Conference Paper |
This study investigates how the introduction of morphological novelty affects the fitness and diversity of a population of modular robots. Novelty is usually measured in behavioral space, while the approach discussed in this paper assesses novelty solely using morphologies. The proposed algorithm is inspired by the histogram of oriented gradients, in combination with elements of principal componen...Show More

Morphological-Novelty in Modular Robot Evolution

Oliver Weissl;A.E. Eiben

Year: 2023 | Conference Paper |

Shaping Imbalance into Balance: Active Robot Guidance of Human Teachers for Better Learning from Demonstrations

Muhan Hou;Koen Hindriks;A.E. Eiben;Kim Baraka

2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Learning from Demonstrations (LfD) transfers skills from human teachers to robots. However, data imbalance in demonstrations can bias policies towards majority situations. Previous work attempted to solve this problem after data collection, but few efforts were made to maintain a balanced distribution from the phase of data acquisition. Our method accounts for the influence of robots on human teac...Show More

Shaping Imbalance into Balance: Active Robot Guidance of Human Teachers for Better Learning from Demonstrations

Muhan Hou;Koen Hindriks;A.E. Eiben;Kim Baraka

Year: 2023 | Conference Paper |

A Process-Oriented Framework for Robot Imitation Learning in Human-Centered Interactive Tasks

Muhan Hou;Koen Hindriks;A.E. Eiben;Kim Baraka

2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Human-centered interactive robot tasks (e.g., social greetings and cooperative dressing) are a type of task where humans are involved in task dynamics and performance evaluation. Such tasks require spatial and temporal coordination between agents in real-time, tackling physical limitations from constrained robot bodies, and connecting human user experience with concrete learning objectives to info...Show More

A Process-Oriented Framework for Robot Imitation Learning in Human-Centered Interactive Tasks

Muhan Hou;Koen Hindriks;A.E. Eiben;Kim Baraka

Year: 2023 | Conference Paper |

Morpho Evolution With Learning Using a Controller Archive as an Inheritance Mechanism

Léni K. Le Goff;Edgar Buchanan;Emma Hart;Agoston E. Eiben;Wei Li;Matteo De Carlo;Alan F. Winfield;Matthew F. Hale;Robert Woolley;Mike Angus;Jon Timmis;Andy M. Tyrrell

IEEE Transactions on Cognitive and Developmental Systems
Year: 2023 | Volume: 15, Issue: 2 | Journal Article |
Cited by: Papers (7)
Most work in evolutionary robotics centers on evolving a controller for a fixed body plan. However, previous studies suggest that simultaneously evolving both controller and body plan could open up many interesting possibilities. However, the joint optimization of body plan and control via evolutionary processes can be challenging in rich morphological spaces. This is because offspring can have bo...Show More

Morpho Evolution With Learning Using a Controller Archive as an Inheritance Mechanism

Léni K. Le Goff;Edgar Buchanan;Emma Hart;Agoston E. Eiben;Wei Li;Matteo De Carlo;Alan F. Winfield;Matthew F. Hale;Robert Woolley;Mike Angus;Jon Timmis;Andy M. Tyrrell

Year: 2023 | Volume: 15, Issue: 2 | Journal Article |

The Influence of Robot Traits and Evolutionary Dynamics on the Reality Gap

Fuda van Diggelen;Eliseo Ferrante;Nihed Harrak;Jie Luo;Daan Zeeuwe;A. E. Eiben

IEEE Transactions on Cognitive and Developmental Systems
Year: 2023 | Volume: 15, Issue: 2 | Journal Article |
Cited by: Papers (4)
The elephant in the room for evolutionary robotics is the reality gap. In the history of the field, several studies investigated this phenomenon on fixed robot morphologies where only the controllers evolved. This article addresses the reality gap in a wider context, in a system where both morphologies and controllers evolve. In this context, the morphology of the robots becomes a variable with a ...Show More

The Influence of Robot Traits and Evolutionary Dynamics on the Reality Gap

Fuda van Diggelen;Eliseo Ferrante;Nihed Harrak;Jie Luo;Daan Zeeuwe;A. E. Eiben

Year: 2023 | Volume: 15, Issue: 2 | Journal Article |

Moonwalkers: Evolving Robots for Locomotion in a Moon-like Environment

Koen Van Der Pool;A.E. Eiben

2021 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2021 | Conference Paper |
Robots are arguably essential for space research in the future, but designing and producing robots for unknown environments represents a grand challenge. The field of Evolutionary Robotics offers a solution by applying the principles of natural evolution to robot design. In this paper, we consider a Moon-like environment and investigate the joint evolution of morphologies (bodies) and controllers ...Show More

Moonwalkers: Evolving Robots for Locomotion in a Moon-like Environment

Koen Van Der Pool;A.E. Eiben

Year: 2021 | Conference Paper |

Evolving Robot Software and Hardware

A.E. Eiben

2020 IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)
Year: 2020 | Conference Paper |
This paper summarizes the keynote I gave on the SEAMS 2020 conference. Noting the power of natural evolution that makes living systems extremely adaptive, I describe how artificial evolution can be employed to solve design and optimization problems in software. Thereafter, I discuss the Evolution of Things, that is, the possibility of evolving physical artefacts and zoom in on a (r)evolutionary wa...Show More

Evolving Robot Software and Hardware

A.E. Eiben

Year: 2020 | Conference Paper |

Hardware Design for Autonomous Robot Evolution

Matthew F. Hale;Mike Angus;Edgar Buchanan;Wei Li;Robert Woolley;Léni K. Le Goff;Matteo De Carlo;Jon Timmis;Alan F. Winfield;Emma Hart;Agoston E. Eiben;Andy M. Tyrrell

2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2020 | Conference Paper |
Cited by: Papers (9)
The long term goal of the Autonomous Robot Evolution (ARE) project is to create populations of physical robots, in which both the controllers and body plans are evolved. The transition of evolutionary designs from purely simulation environments into the real world creates the possibility for new types of system able to adapt to unknown and changing environments. In this paper, a system for creatin...Show More

Hardware Design for Autonomous Robot Evolution

Matthew F. Hale;Mike Angus;Edgar Buchanan;Wei Li;Robert Woolley;Léni K. Le Goff;Matteo De Carlo;Jon Timmis;Alan F. Winfield;Emma Hart;Agoston E. Eiben;Andy M. Tyrrell

Year: 2020 | Conference Paper |

The Effects of Adaptive Control on Learning Directed Locomotion

Fuda van Diggelen;Robert Babuska;A.E. Eiben

2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
This study is motivated by evolutionary robot systems where robot bodies and brains evolve simultaneously. In such systems robot `birth' must be followed by `infant learning' by a learning method that works for various morphologies evolution may produce. Here we address the task of directed locomotion in modular robots with controllers based on Central Pattern Generators. We present a bio-inspired...Show More

The Effects of Adaptive Control on Learning Directed Locomotion

Fuda van Diggelen;Robert Babuska;A.E. Eiben

Year: 2020 | Conference Paper |

Influences of Artificial Speciation on Morphological Robot Evolution

Matteo De Carlo;Daan Zeeuwe;Eliseo Ferrante;Gerben Meynen;Jacintha Ellers;A.E. Eiben

2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
One key challenge in Evolutionary Robotics (ER) is to evolve morphology and controllers of robots. Most experiments in the field converge rapidly to a single solution for the entire population. Early convergence results in a premature loss of diversity, which creates inconsistent results across multiple runs, sometimes converging to a local optimum. In Nature we can observe the opposite behavior: ...Show More

Influences of Artificial Speciation on Morphological Robot Evolution

Matteo De Carlo;Daan Zeeuwe;Eliseo Ferrante;Gerben Meynen;Jacintha Ellers;A.E. Eiben

Year: 2020 | Conference Paper |

Evolution of Diverse, Manufacturable Robot Body Plans

Edgar Buchanan;Léni K. Le Goff;Emma Hart;Agoston E. Eiben;Matteo De Carlo;Wei Li;Matthew F. Hale;Mike Angus;Robert Woolley;Alan F. Winfield;Jon Timmis;Andy M. Tyrrell

2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
Advances in rapid prototyping have opened up new avenues of research within Evolutionary Robotics in which not only controllers but also the body plans (morphologies) of robots can evolve in real-time and real-space. However, this also introduces new challenges, in that robot models that can be instantiated from an encoding in simulation might not be manufacturable in practice (due to constraints ...Show More

Evolution of Diverse, Manufacturable Robot Body Plans

Edgar Buchanan;Léni K. Le Goff;Emma Hart;Agoston E. Eiben;Matteo De Carlo;Wei Li;Matthew F. Hale;Mike Angus;Robert Woolley;Alan F. Winfield;Jon Timmis;Andy M. Tyrrell

Year: 2020 | Conference Paper |

Robotic task affects the resulting morphology and behaviour in evolutionary robotics

Matteo De Carlo;Daan Zeeuwe;Eliseo Ferrante;Gerben Meynen;Jacintha Ellers;A.E. Eiben

2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2020 | Conference Paper |
Cited by: Papers (5)
In evolution, the evolutionary success of individuals is influenced in equal parts by the environment they are living in and by the adapting capability that they possess. In this work, instead of analyzing the adaptability of a population, we investigate how different environments influence the evolution of morphologies and controllers of modular robots. Previous work analyzed how robot evolution ...Show More

Robotic task affects the resulting morphology and behaviour in evolutionary robotics

Matteo De Carlo;Daan Zeeuwe;Eliseo Ferrante;Gerben Meynen;Jacintha Ellers;A.E. Eiben

Year: 2020 | Conference Paper |

Towards a human-like movements generator based on environmental features

A. Zonta;S. K. Smit;A.E. Eiben

2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2020 | Conference Paper |
Modelling realistic human behaviour in simulation is an ongoing challenge that sits between several fields like social sciences, philosophy, and artificial intelligence. Human movement is a special type of behaviour driven by intent (e.g. to get groceries) and the surrounding environment (e.g. curiosity to see new interesting places). Services available online and offline do not normally consider ...Show More

Towards a human-like movements generator based on environmental features

A. Zonta;S. K. Smit;A.E. Eiben

Year: 2020 | Conference Paper |

Generation of Human-Like Movements Based on Environmental Features

A. Zonta;S. K. Smit;M. Hoogendoorn;A.E. Eiben

2019 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2019 | Conference Paper |
Cited by: Papers (1)
Modelling human behaviour in simulation is still an ongoing challenge that spaces between several fields like social science, artificial intelligence, and philosophy. Humans normally move driven by their intent (e.g. to get groceries) and the surrounding environment (e.g. curiosity to see new interesting places). Normal services available online and offline do not consider the environment when pla...Show More

Generation of Human-Like Movements Based on Environmental Features

A. Zonta;S. K. Smit;M. Hoogendoorn;A.E. Eiben

Year: 2019 | Conference Paper |

Simulated and Real-World Evolution of Predator Robots

Gongjin Lan;Jiunhan Chen;A. E. Eiben

2019 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2019 | Conference Paper |
Cited by: Papers (3)
This paper addresses the problem of designing behavioural strategies for a group of robots with a specific task, capturing another robot. Our proposed approach is to employ a "smart" prey with a pre-programmed strategy based on a novel Gaussian model of danger zones and use an evolutionary algorithm (EA) to optimize the predators’ behavior. The EA is applied in two stages: first in simulation, the...Show More

Simulated and Real-World Evolution of Predator Robots

Gongjin Lan;Jiunhan Chen;A. E. Eiben

Year: 2019 | Conference Paper |

CluStream-GT: Online Clustering for Personalization in the Health Domain

Eoin Martino Grua;Mark Hoogendoorn;Ivano Malavolta;Patricia Lago;A.E. Eiben

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)
Year: 2019 | Conference Paper |
Clustering of users underlies many of the personalisation algorithms that are in use nowadays. Such clustering is mostly performed in an offline fashion. For a health and wellbeing setting, offline clustering might however not be suitable, as limited data is often available and patient states can also quickly evolve over time. Existing online clustering algorithms are not suitable for the health d...Show More

CluStream-GT: Online Clustering for Personalization in the Health Domain

Eoin Martino Grua;Mark Hoogendoorn;Ivano Malavolta;Patricia Lago;A.E. Eiben

Year: 2019 | Conference Paper |

End-to-end Personalization of Digital Health Interventions using Raw Sensor Data with Deep Reinforcement Learning : A comparative study in digital health interventions for behavior change

Ali el Hassouni;Mark Hoogendoorn;A. E. Eiben;Martijn van Otterlo;Vesa Muhonen

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)
Year: 2019 | Conference Paper |
We introduce an end-to-end reinforcement learning (RL) solution for the problem of sending personalized digital health interventions. Previous work has shown that personalized interventions can be obtained through RL using simple, discrete state information such as the recent activity performed. In reality however, such features are often not observed, but instead could be inferred from noisy, low...Show More

End-to-end Personalization of Digital Health Interventions using Raw Sensor Data with Deep Reinforcement Learning : A comparative study in digital health interventions for behavior change

Ali el Hassouni;Mark Hoogendoorn;A. E. Eiben;Martijn van Otterlo;Vesa Muhonen

Year: 2019 | Conference Paper |

Benefits of Social Learning in Physical Robots

Jacqueline Heinerman;Bart Bussmann;Rick Groenendijk;Emile van Krieken;Jesper Slik;Alessandro Tezza;Evert Haasdijk;A.E. Eiben

2018 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2018 | Conference Paper |
Robot-to-robot learning, a specific case of social learning in robotics, enables the ability to transfer robot controllers directly from one robot to another. Previous studies showed that the exchange of controller information can increase learning speed and performance. However, most of these studies have been performed in simulation, where robots are identical. Therefore, the results do not nece...Show More

Benefits of Social Learning in Physical Robots

Jacqueline Heinerman;Bart Bussmann;Rick Groenendijk;Emile van Krieken;Jesper Slik;Alessandro Tezza;Evert Haasdijk;A.E. Eiben

Year: 2018 | Conference Paper |

Morphological Attractors in Darwinian and Lamarckian Evolutionary Robot Systems

Milan Jelisavcic;Karine Miras;A.E. Eiben

2018 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2018 | Conference Paper |
Cited by: Papers (4)
Morphological evolution in a robotic system produces novel robot bodies after each reproduction event. This implies the necessity for life-time learning so that newborn robots can acquire a controller that fits their body. Thus, we obtain a system where evolution and learning are combined. This combination can be Darwinian or Lamarckian and in this paper, we compare the two. In particular, we inve...Show More

Morphological Attractors in Darwinian and Lamarckian Evolutionary Robot Systems

Milan Jelisavcic;Karine Miras;A.E. Eiben

Year: 2018 | Conference Paper |

Modelling Human Movements With Turing Learning

Alessandro Zonta;S. K. Smit;Evert Haasdijk;A. E. Eiben

2018 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
Modelling human behaviour is still an ongoing challenge that spaces between several fields like social science, artificial intelligence, and philosophy. Since the research of a metric able to define all the aspect of the human nature is still an ambitious task, most current studies use concepts like social forces or handwritten rules for modelling. Following the growing trend behind a new branch o...Show More

Modelling Human Movements With Turing Learning

Alessandro Zonta;S. K. Smit;Evert Haasdijk;A. E. Eiben

Year: 2018 | Conference Paper |

Insights in evolutionary exploration of robot morphology spaces

Karine Miras;Arwin Gansekoele;Kyrre Glette;A.E. Eiben

2018 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
In a recent study we have encountered an unexpected result regarding the evolutionary exploration of robot morphology spaces. Specifically, we found that an algorithm driven by selection based on morphological novelty explored fewer spots in the space of morphologies than another algorithm based on a combination of morphological novelty and some behavioral criterion (speed of movement). Here we re...Show More

Insights in evolutionary exploration of robot morphology spaces

Karine Miras;Arwin Gansekoele;Kyrre Glette;A.E. Eiben

Year: 2018 | Conference Paper |

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