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2. Body symmetry in morphologically evolving modular robots

Author

van de Velde, T., Rossi, C., Eiben, A. E., Castillo, Pedro A., Kaufmann, Paul, Artificial intelligence, Network Institute, Computational Intelligence, Castillo, Pedro A., and Kaufmann, Paul

Subjects
Self-reconfiguring modular robot, Evolutionary robotics, Symmetry, Modular robots, Computer science, Robot, Symmetry (geometry), Set (psychology), Topology, Positive correlation, Weak correlation, Task (project management)
Abstract

Almost all animals natural evolution has produced on Earth have a symmetrical body. In this paper we investigate the evolution of body symmetry in an artificial system where robots evolve. To this end, we define several measures to quantify symmetry in modular robots and see how these relate to fitness that corresponds to a locomotion task. We find that, although there is only a weak correlation between symmetry and fitness over the course of a single evolutionary run, there is a positive correlation between the level of symmetry and maximum fitness when a set of runs is taken into account.

Published
2019

4. COVID-19: A Survey on Public Medical Imaging Data Resources

Author

Kalkreuth, Roman and Kaufmann, Paul

Subjects
Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

This regularly updated survey provides an overview of public resources that offer medical images and metadata of COVID-19 cases. The purpose of this survey is to simplify the access to open COVID-19 image data resources for all scientists currently working on the coronavirus crisis.

Published
2020
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5. Search Space Analysis of Evolvable Robot Morphologies

Author

da Silva Miras de Araujo, K., Haasdijk, Evert, Glette, Kyrre, Eiben, A.E., Sim, Kevin, Kaufmann, Paul, Artificial intelligence, Network Institute, Computational Intelligence, Sim, Kevin, and Kaufmann, Paul

Subjects
Self-reconfiguring modular robot, Morphology, education.field_of_study, Modular robots, business.industry, Computer science, Population, Generative encoding, Evolutionary robotics, Novelty, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, 02 engineering and technology, Set (abstract data type), Novelty search, Search algorithm, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Evolutionary Robotics, education, business, Representation (mathematics), human activities
Abstract

We present a study on morphological traits of evolved modular robots. We note that the evolutionary search space –the set of obtainable morphologies– depends on the given representation and reproduction operators and we propose a framework to assess morphological traits in this search space regardless of a specific environment and/or task. To this end, we present eight quantifiable morphological descriptors and a generic novelty search algorithm to produce a diverse set of morphologies for any given representation. With this machinery, we perform a comparison between a direct encoding and a generative encoding. The results demonstrate that our framework permits to find a very diverse set of bodies, allowing a morphological diversity investigation. Furthermore, the analysis showed that despite the high levels of diversity, a bias to certain traits in the population was detected. Surprisingly, the two encoding methods showed no significant difference in the diversity levels of the evolved morphologies or their morphological traits.

Published
2018

6. Revolve: A Versatile Simulator for Online Robot Evolution

Author

Hupkes, Elte, Jelisavcic, Milan, Eiben, A. E., Sim, Kevin, Kaufmann, Paul, Artificial intelligence, Network Institute, Computational Intelligence, Sim, Kevin, and Kaufmann, Paul

Subjects
0301 basic medicine, Online and offline, Self-reconfiguring modular robot, education.field_of_study, Modular robots, Computer science, Population, Evolutionary algorithm, Reality gap, Evolutionary algorithms, Offline learning, Term (time), 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Online learning, Robot, Actuator, education, 030217 neurology & neurosurgery, Simulation
Abstract

Developing robotic systems that can evolve in real-time and real-space is a long term objective with technological as well as algorithmic milestones on the road. Technological prerequisites include advanced 3D-printing, automated assembly, and robust sensors and actuators. The necessary evolutionary mechanisms need not wait for these, they can be developed and investigated in simulations. In this paper, we present a system to simulate online evolution of constructible robots, where (1) the population members (robots) concurrently exist and evolve their morphologies and controllers, (2) all robots can be physically constructed. Experiments with this simulator provide us with insights into differences of using online and offline evolutionary setups.

Published
2018

7. Improving Multi-objective Evolutionary Influence Maximization in Social Networks

Author

Bucur, Doina, Iacca, Giovanni, Marcelli, Andrea, Squillero, Giovanni, Tonda, Alberto, Sim, Kevin, Kaufmann, Paul, Johann Bernoulli Institute, University of Groningen, INCAS3, Politecnico di Torino = Polytechnic of Turin (Polito), DAUIN Dipartimento di Automatica e Informatica, Génie et Microbiologie des Procédés Alimentaires (GMPA), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Twente University of Technology, Integrated Signal Processing Systems, Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), DAUIN, COST Agency [CA15140], Politecnico di Torino [Torino] (Polito), and Institut National de la Recherche Agronomique (INRA)-AgroParisTech

Subjects
Mathematical optimization, Speedup, Computer science, [SDV]Life Sciences [q-bio], Population, Evolutionary algorithm, Seeding, Context (language use), 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], Theoretical Computer Science, Influence maximization, Multi-objective evolutionary algorithms, Social network, Computer Science (all), 020204 information systems, [SDV.IDA]Life Sciences [q-bio]/Food engineering, 0202 electrical engineering, electronic engineering, information engineering, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, education, ComputingMilieux_MISCELLANEOUS, education.field_of_study, business.industry, Maximization, Influence maximization, Social network, Multi-objective evolutionary algorithms, Seeding, Range (mathematics), Social network Multi-objective evolutionary algorithms, 020201 artificial intelligence & image processing, business, Heuristics
Abstract

International audience; In the context of social networks, maximizing influence means contacting the largest possible number of nodes starting from a set of seed nodes, and assuming a model for influence propagation. The real-world applications of influence maximization are of uttermost importance, and range from social studies to marketing campaigns. Building on a previous work on multi-objective evolutionary influence maximization, we propose improvements that not only speed up the optimization process considerably, but also deliver higher-quality results. State-of-the-art heuristics are run for different sizes of the seed sets, and the results are then used to initialize the population of a multi-objective evolutionary algorithm. The proposed approach is tested on three publicly available real-world networks, where we show that the evolutionary algorithm is able to improve upon the solutions found by the heuristics, while also converging faster than an evolutionary algorithm started from scratch.

Published
2018

8. Multi-strategy differential evolution

Author

Yaman, A., Iacca, G., Coler, M., Fletcher, G.H.L., Pechenizkiy, M., Sim, Kevin, Kaufmann, Paul, Integrated Circuits, Database Group, Data Mining, and Culture, Language & Technology

Subjects
Continuous optimization, Mathematical optimization, education.field_of_study, Optimization problem, Computer science, Parameter control, Population, 020206 networking & telecommunications, 02 engineering and technology, Evolutionary computation, Public records, Differential evolution, Continuous optimization, Differential evolution, Parameter control, Strategy adaptation, 0202 electrical engineering, electronic engineering, information engineering, Benchmark (computing), 020201 artificial intelligence & image processing, Strategy adaptation, education, Adaptation (computer science), Evolutionary Computation, optimization problem
Abstract

We propose the Multi-strategy Differential Evolution (MsDE) algorithm to construct and maintain a self-adaptive ensemble of search strategies while solving an optimization problem. The ensemble of strategies is represented as agents that interact with the candidate solutions to improve their fitness. In the proposed algorithm, the performance of each agent is measured so that successful strategies are promoted within the ensemble. We propose two performance measures, and show their effectiveness in selecting successful strategies. We then present three population adaptation mechanisms, based on sampling, clone-best and clone-multiple adaptation schemes. The MsDE with different performance measures and population adaptation schemes is tested on the CEC2013 benchmark functions and compared with basic DE and with Self-Adaptive DE (SaDE). Our results show that MsDE is capable of efficiently adapting the strategies and parameters of DE and providing competitive results with respect to the state-of-the-art.

Published
2018

9. Preface

Author

Sim, Kevin, Kaufmann, Paul, Ascheid, Gerd, Bacardit, Jaume, Cagnoni, Stefano, Cotta, Carlos, D’Andreagiovanni, Fabio, and Mora, Antonio M (1)

Subjects
Scopus(2)
Abstract

21st International Conference on Applications of Evolutionary Computation, EvoApplications 2018; parma; Italy; 4 April 2018 through 6 April 2018

Published
2018

10. Preface

Author

Sim, Kevin, Kaufmann, Paul, Ascheid, Gerd, Bacardit, Jaume, Cagnoni, Stefano, Cotta, Carlos, D’Andreagiovanni, Fabio, Divina, Federico, Esparcia-Alcázar, Anna I., Vega, Francisco Fernández De, Glette, Kyrre, Hubert, Julien, Hidalgo, J. Ignacio, Iacca, Giovanni, Kampouridis, Michael, Kramer, Oliver, Mavrovouniotis, Michalis, Mora García, Antonio M., Nguyen, Trung Thanh, Otero, Fernando, Schaefer, Robert, Silva, Sara, Tonda, Alberto, Urquhart, Neil, and Zhang, Mengjie

Published
2018

11. Preface

Author

Sim, Kevin, Kaufmann, Paul, Ascheid, Gerd, Bacardit, Jaume, Cagnoni, Stefano, Cotta, Carlos, D’Andreagiovanni, Fabio, and Mora, Antonio M (1)

Subjects
Scopus(2)
Abstract

21st International Conference on Applications of Evolutionary Computation, EvoApplications 2018; parma; Italy; 4 April 2018 through 6 April 2018

Published
2018

12. Preface

Author

Sim, Kevin, Kaufmann, Paul, Ascheid, Gerd, Bacardit, Jaume, Cagnoni, Stefano, Cotta, Carlos, D’Andreagiovanni, Fabio, Divina, Federico, Esparcia-Alcázar, Anna I., Vega, Francisco Fernández De, Glette, Kyrre, Hubert, Julien, Hidalgo, J. Ignacio, Iacca, Giovanni, Kampouridis, Michael, Kramer, Oliver, Mavrovouniotis, Michalis, Mora García, Antonio M., Nguyen, Trung Thanh, Otero, Fernando, Schaefer, Robert, Silva, Sara, Tonda, Alberto, Urquhart, Neil, Zhang, Mengjie, and Artificial intelligence

Published
2018

13. Preface

Author

Sim, Kevin, Kaufmann, Paul, Ascheid, Gerd, Bacardit, Jaume, Cagnoni, Stefano, Cotta, Carlos, D’Andreagiovanni, Fabio, and Mora, Antonio M (1)

Subjects
Scopus(2)
Abstract

21st International Conference on Applications of Evolutionary Computation, EvoApplications 2018; parma; Italy; 4 April 2018 through 6 April 2018

Published
2018

14. Preface

Author

Squillero, Giovanni, Sim, Kevin, Ascheid, Gerd, Bacardit, Jaume, Brabazon, Anthony, Burelli, Paolo, Cagnoni, Stefano, Coler, Matt, Cotta, Carlos, D’Andreagiovanni, Fabio, Divina, Federico, Esparcia-Alcázar, Anna I., de Vega, Francisco Fernández, Glette, Kyrre, Haasdijk, Evert, Heinerman, Jacqueline, Hidalgo, J. Ignacio, Hu, Ting, Iacca, Giovanni, Kampouridis, Michael, Kaufmann, Paul, Mavrovouniotis, Michalis, Mora Garcia, Antonio M., Schaefer, Robert, Silva, Sara, Tarantino, Ernesto, Nguyen, Trung Thanh, Tonda, Alberto, Urquhart, Neil, and Zhang, Mengjie

Published
2017

15. EvoApplications: 2017: 20th European Conference on the Applications of Evolutionary Computation, Amsterdam; Netherlands; 19-21 April 2017

Author

Squillero, Giovanni, Sim, Kevin, Ascheid, Gerd, Bacardit, Jaume, Brabazon, Anthony, Burelli, Paolo, Cagnoni, Stefano, Coler, Matt, Cotta, Carlos, D'Andreagiovanni, Fabio, Divina, Federico, Esparcia-Alcázar, Anna I., de Vega, Francisco Fernández, Glette, Kyrre, Haasdijk, Evert, Heinerman, Jacqueline, Hidalgo, J. Ignacio, Hu, Ting, Iacca, Giovanni, Kampouridis, Michael, Kaufmann, Paul, Mavrovouniotis, Michalis, Mora Garcia, Antonio M., Schaefer, Robert, Silva, Sara, Tarantino, Ernesto, Nguyen, Trung Thanh, Tonda, Alberto, Urquhart, Neil, and Zhang, Mengjie

Subjects
applications, Evolutionary computation
Abstract

Proceedings of the European Conference on the Applications of Evolutionary Computation, EvoApplications 2017; Amsterdam; Netherlands; 19 -21 April 2017

Published
2017

16. Preface

Author

Squillero, Giovanni, Sim, Kevin, Ascheid, Gerd, Bacardit, Jaume, Brabazon, Anthony, Burelli, Paolo, Cagnoni, Stefano, Coler, Matt, Cotta, Carlos, D’Andreagiovanni, Fabio, Divina, Federico, Esparcia-Alcázar, Anna I., de Vega, Francisco Fernández, Glette, Kyrre, Haasdijk, Evert, Heinerman, Jacqueline, Hidalgo, J. Ignacio, Hu, Ting, Iacca, Giovanni, Kampouridis, Michael, Kaufmann, Paul, Mavrovouniotis, Michalis, Mora Garcia, Antonio M., Schaefer, Robert, Silva, Sara, Tarantino, Ernesto, Nguyen, Trung Thanh, Tonda, Alberto, Urquhart, Neil, and Zhang, Mengjie

Published
2017

17. Preface

Author

Squillero, Giovanni, Sim, Kevin, Ascheid, Gerd, Bacardit, Jaume, Brabazon, Anthony, Burelli, Paolo, Cagnoni, Stefano, Coler, Matt, Cotta, Carlos, D’Andreagiovanni, Fabio, Divina, Federico, Esparcia-Alcázar, Anna I., de Vega, Francisco Fernández, Glette, Kyrre, Haasdijk, Evert, Heinerman, Jacqueline, Hidalgo, J. Ignacio, Hu, Ting, Iacca, Giovanni, Kampouridis, Michael, Kaufmann, Paul, Mavrovouniotis, Michalis, Mora Garcia, Antonio M., Schaefer, Robert, Silva, Sara, Tarantino, Ernesto, Nguyen, Trung Thanh, Tonda, Alberto, Urquhart, Neil, Zhang, Mengjie, Artificial intelligence, Computational Intelligence, Network Institute, and Physics of Living Systems

Published
2017

18. Preface

Author

Esparcia-Alcázar, Anna I., Mora, Antonio M., Agapitos, Alexandros, Burelli, Paolo, Bush, William S., Cagnoni, Stefano, Cotta, Carlos, De Falco, Ivanoe, Cioppa, Antonio Della, Divina, Federico, Drechsler, Rolf, Eiben, A. E., de Vega, Francisco Fernández, Glette, Kyrre, Haasdijk, Evert, Ignacio Hidalgo, J., Kaufmann, Paul, Nguyen, Trung Thanh, Pošík, Petr, Schaefer, Robert, Sim, Kevin, Simões, Anabela, Tettamanzi, Andrea G B, Urquhart, Neil, Zhang, Mengjie, Zincir-Heywood, Nur, Artificial intelligence, Computational Intelligence, Network Institute, and Physics of Living Systems

Published
2014

19. Applications of evolutionary computation:19th European conference, Evoapplications 2016 Porto, Portugal, March 30 – April 1, 2016 proceedings, part II

Author

Squillero, Giovanni, Burelli, Paolo, Bacardit, Jaume, Brabazon, Anthony, Cagnoni, Stefano, Cotta, Carlos, De Falco, Ivanoe, Cioppa, Antonio Della, Divina, Federico, Eiben, A. E., Esparcia-Alcàza, Anna I., de Vega, Francisco Fernàndez, Glette, Kyrre, Haasdijk, Evert, Hidalgo, J. Ignacio, Hu, Ting, Kampouridis, Michael, Kaufmann, Paul, Mavrovouniotis, Michalis, Mora García, Antonio M., Nguyen, Trung Thanh, Schaefer, Robert, Sim, Kevin, Tarantino, Ernesto, Urquhart, Neil, and Zhang, Mengjie

Published
2016

20. Applications of evolutionary computation:19th European conference, evoapplications 2016 Porto, Portugal, march 30 – april 1, 2016 proceedings, part I

Author

Squillero, Giovanni, Burelli, Paolo, Bacardit, Jaume, Brabazon, Anthony, Cagnoni, Stefano, Cotta, Carlos, De Falco, Ivanoe, Cioppa, Antonio Della, Divina, Federico, Eiben, A. E., Esparcia-Alcázar, Anna I., de Vega, Francisco Fernández, Glette, Kyrre, Haasdijk, E.W., Hidalgo, J. Ignacio, Hu, Ting, Kampouridis, Michael, Kaufmann, Paul, Mavrovouniotis, Michalis, García, Antonio M Mora, Nguyen, Trung Thanh, Schaefer, Robert, Sim, Kevin, Tarantino, Ernesto, Urquhart, Neil, and Zhang, Mengjie

Published
2016

21. Applications of evolutionary computation: 19th European conference, Evoapplications 2016 Porto, Portugal, March 30 – April 1, 2016 proceedings, part II

Author

Squillero, Giovanni, Burelli, Paolo, Bacardit, Jaume, Brabazon, Anthony, Cagnoni, Stefano, Cotta, Carlos, De Falco, Ivanoe, Cioppa, Antonio Della, Divina, Federico, Eiben, A. E., Esparcia-Alcàza, Anna I., de Vega, Francisco Fernàndez, Glette, Kyrre, Haasdijk, Evert, Hidalgo, J. Ignacio, Hu, Ting, Kampouridis, Michael, Kaufmann, Paul, Mavrovouniotis, Michalis, Mora García, Antonio M., Nguyen, Trung Thanh, Schaefer, Robert, Sim, Kevin, Tarantino, Ernesto, Urquhart, Neil, Zhang, Mengjie, Artificial intelligence, Network Institute, and Computational Intelligence

Published
2016

22. Applications of evolutionary computation: 19th European conference, evoapplications 2016 Porto, Portugal, march 30 – april 1, 2016 proceedings, part I

Author

Squillero, Giovanni, Burelli, Paolo, Bacardit, Jaume, Brabazon, Anthony, Cagnoni, Stefano, Cotta, Carlos, De Falco, Ivanoe, Cioppa, Antonio Della, Divina, Federico, Eiben, A. E., Esparcia-Alcázar, Anna I., de Vega, Francisco Fernández, Glette, Kyrre, Haasdijk, E.W., Hidalgo, J. Ignacio, Hu, Ting, Kampouridis, Michael, Kaufmann, Paul, Mavrovouniotis, Michalis, García, Antonio M Mora, Nguyen, Trung Thanh, Schaefer, Robert, Sim, Kevin, Tarantino, Ernesto, Urquhart, Neil, Zhang, Mengjie, Artificial intelligence, Network Institute, and Computational Intelligence

Published
2016

23. Preface

Author

Squillero, Giovanni, Bacardit, Jaume, Cagnoni, Stefano, De Falco, Ivanoe, Divina, Federico, Esparcia-Alcázar, Anna I., Glette, Kyrre, Hidalgo, J. Ignacio, Kampouridis, Michael, Mavrovouniotis, Michalis, Nguyen, Trung Thanh, Sim, Kevin, Urquhart, Neil, Burelli, Paolo, Brabazon, Anthony, Cotta, Carlos, Cioppa, Antonio Della, Eiben, A. E., de Vega, Francisco Fernández, Haasdijk, Evert, Hu, Ting, Kaufmann, Paul, Mora Garcia, Antonio M., Schaefer, Robert, Tarantino, Ernesto, Zhang, Mengjie, Artificial intelligence, Physics of Living Systems, Computational Intelligence, and Network Institute

Published
2016

24. Preface

Author

Squillero, Giovanni, Bacardit, Jaume, Cagnoni, Stefano, De Falco, Ivanoe, Divina, Federico, Esparcia-Alcázar, Anna I., Glette, Kyrre, Hidalgo, J. Ignacio, Kampouridis, Michael, Mavrovouniotis, Michalis, Nguyen, Trung Thanh, Sim, Kevin, Urquhart, Neil, Burelli, Paolo, Brabazon, Anthony, Cotta, Carlos, Cioppa, Antonio Della, Eiben, A. E., de Vega, Francisco Fernández, Haasdijk, Evert, Hu, Ting, Kaufmann, Paul, Mora Garcia, Antonio M., Schaefer, Robert, Tarantino, Ernesto, and Zhang, Mengjie

Published
2016

25. Applications of evolutionary computation

Author

Mora, Antonio M., Squillero, Giovanni, Agapitos, Alexandros, Burelli, Paolo, Bush, William S., Cagnoni, Stefano, Cotta, Carlos, De Falco, Ivanoe, Cioppa, Antonio Della, Divina, Federico, Eiben, A. E., Esparcia-Alcázar, Anna I., de Vega, Francisco Fernández, Glette, Kyrre, Haasdijk, Evert, Hidalgo, J. Ignacio, Kampouridis, Michael, Kaufmann, Paul, Mavrovouniotis, Michalis, Nguyen, Trung Thanh, Schaefer, Robert, Sim, Kevin, Tarantino, Ernesto, Urquhart, Neil, Zhang, Mengjie, Vrije Universiteit Amsterdam, Artificial intelligence, Computational Intelligence, Network Institute, and Physics of Living Systems

Published
2015

27. CORRESPONDENCE

Author

Kaufmann, Paul

Subjects
Correspondence
Published
1983

30. Reconsideration and extension of Cartesian genetic programming

Author

Kalkreuth, Roman Tobias, Rudolph, Günter, and Kaufmann, Paul

Subjects
Evolution��re Programmierung, Genetische Programmierung, Evolutionary computation, Genetic programming, Cartesian genetic programming
Abstract

This dissertation aims on analyzing fundamental concepts and dogmas of a graph-based genetic programming approach called Cartesian Genetic Programming (CGP) and introduces advanced genetic operators for CGP. The results of the experiments presented in this thesis lead to more knowledge about the algorithmic use of CGP and its underlying working mechanisms. CGP has been mostly used with a parametrization pattern, which has been prematurely generalized as the most efficient pattern for standard CGP and its variants. Several parametrization patterns are evaluated with more detailed and comprehensive experiments by using meta-optimization. This thesis also presents a first runtime analysis of CGP. The time complexity of a simple (1+1)-CGP algorithm is analyzed with a simple mathematical problem and a simple Boolean function problem. In the subfield of genetic operators for CGP, new recombination and mutation techniques that work on a phenotypic level are presented. The effectiveness of these operators is demonstrated on a widespread set of popular benchmark problems. Especially the role of recombination can be seen as a big open question in the field of CGP, since the lack of an effective recombination operator limits CGP to mutation-only use. Phenotypic exploration analysis is used to analyze the effects caused by the presented operators. This type of analysis also leads to new insights into the search behavior of CGP in continuous and discrete fitness spaces. Overall, the outcome of this thesis leads to a reconsideration of how CGP is effectively used and extends its adaption from Darwin's and Lamarck's theories of biological evolution.

Published
2021

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