Evolution in Cognition Workshop, GECCO 2016
Evolution by natural selection has shaped life over billions of years leading to the emergence of complex organism capable of exceptional cognitive abilities. These natural evolutionary processes have inspired the development of Evolutionary Algorithms (EAs), which are optimization algorithms widely popular due to their efficiency and robustness. Beyond their ability to optimize, EAs have also proven to be creative and efficient at generating innovative solutions to novel problems. The combination of these two abilities makes them a tool of choice for the resolution of complex problems.
Even though there is evidence that the principle of selection on variation is at play in the human brain, as proposed in Changeux’s and Edelman’s models of Neuronal Darwinism, and more recently expanded in the theory of Darwinian Neurodynamics by Szathmáry, Fernando and others, not much attention has been paid to the possible interaction between evolutionary processes and cognition over physiological time scales. Since the development of human cognition requires years of maturation, it can be expected that artificial cognitive agents will also require months if not years of learning and adaptation. It is in this context that the optimizing and creative abilities of EAs could become an ideal framework that complement, aid in understanding, and facilitate the implementation of cognitive processes. Additionally, a better understanding of how evolution can be implemented as part of an artificial cognitive architecture can lead to new insights into cognition in humans and other animals.
The goals of the workshop are to depict the current state of the art of evolution in cognition and to sketch the main challenges and future directions. In particular, we aim at bringing together the different theoretical and empirical approaches that can potentially contribute to the understanding of how evolution and cognition can act together in an algorithmic way in order to solve complex problems. In this workshop we welcome approaches that contribute to an improved understanding of evolution in cognition using robotic agents, in silico computation as well as mathematical models.
Keywords: Evolutionary Computation, Evolution, Cognition, Darwinian Neurodynamics, Neuronal Darwisnism, robotics.
We are pleased to announce that the following internationally recognized researchers will present their work related to the workshop topic and propose their own views on the related questions. Each invited speaker will give a talk and also participate in a panel discussion at the end of the workshop.
July 21, 2016
- Chrisantha Fernando: Invited talk
- Dario Floreano: Invited talk
- Jeff Krichmar: Invited talk
- Poster session
- Poster session (continued)
- Arne Dietrich: Invited talk
- Stephane Doncieux: Invited talk
- Panel discussion
Extended abstracts presented as posters
- Wilson, D. and Cussat-Blanc, S. and Luga, H.
The Evolution of Artificial Neurogenesis.
- De Vladar, H. P. and Fedor, A. and Szilágyi, A. and Zachar, I. and Szathmàry, E.
An attractor network based model with Darwinian dynamics.
- Shim, Y. and Auerbach, J. E. and Husbands, P.
Darwinian Dynamics of Embodied Chaotic Exploration
- Duro, R. J. and Becerra, J. A. and Monroy, J. and Caamaño, P.
Considering Memory Networks in the LTM structure of the Multilevel Darwinist Brain.
- Auerbach, J. E. and Iacca, G. and Floreano, D.
Gaining Insight into Quality Diversity
- Salgado, R. and Prieto, A. and Bellas, F. and CalvoVarela, L. and Duro, R. J.
Neuroevolutionary Motivational Engine for Autonomous Robots.
Hot of the press posters:
- Cully, A. and Clune, J. and Tarapore, D. and Mouret, J.-B.
Robots that can adapt like animals.
Nature. Vol 521 Pages 503-507. 2015.
- Huizinga, J. and Mouret J.-B. and Clune, J.
Does Aligning Phenotypic and Genotypic Modularity Improve the Evolution of Neural Networks?
Proc. of the GECCO conference. 2016.
- Maestre, C. and Cully, A. and Gonzales, C. and Doncieux, S.
Bootstrapping interactions with objects from raw sensorimotor data: a Novelty Search based approach.
IEEE ICDL-EpiRob conf. 2015.
- Ecarlat, P. and Cully, A. and Maestre, C. and Doncieux, S.
Learning a high diversity of object manipulations though an evolutionary-based babbling.
Proc. of Learning Object Affordances WS, IROS 2015.
- Szerlip, P. A. and Morse, G. and Pugh, J. K. and Stanley, K. O.
Unsupervised feature learning through divergent discriminative feature accumulation.
Proc. of the 29th AAAI Conference on AI. 2015.
GECCO is sponsored by the Association for Computing Machinery Special
Interest Group on Genetic and Evolutionary Computation (SIGEVO). SIG
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