I485/I585: Biologically Inspired Computing Biological organisms cope with the demands of their environments using solutions quite unlike the traditional human-engineered approaches to problem solving. Biological systems tend to be adaptive, reactive, and distributed. Bio-inspired computing is a field devoted to tackling complex problems using computational methods modeled after design principles encountered in nature. This course is strongly grounded on the foundations of complex systems and theoretical biology. Aims: Students will be introduced to fundamental topics in bio-inspired computing, and build up their proficiency in the application of various algorithms in real-world problems. Pre-requisites: INFO-I 211, or CSCI-C 212, or CSCI-H 212, or Instructor approval. What is Life?
Introduction to Genetic Algorithm Introduction to Genetic Algorithms. Physics, Biology, Economy or Sociology often have to deal with the classical problem of optimization. Economy particularly has become specialist of that field1. Purely analytical methods widely proved their efficiency. Other methods, combining mathematical analysis and random search have appeared. What could be the link between optimization methods and artificial life ? A- Evolution and optimization. We are now 45 millions years ago examining a Basilosaurus : Basilosaurus The Basilosaurus was quite a prototype of a whale. Movements in such a viscous element (water) are very hard and require big efforts. Tursiops flipper The image shows that two fingers of the common dolphin are hypertrophied to the detriment of the rest of the member. The basilosaurus was a hunter, he had to be fast and precise. This process of adaptation, this morphological optimization is so perfect that nowadays, the similarity between a shark, a dolphin or a submarine is striking.
Randall D. Beer Dr. Paul Williams Research Interests My primary research interest is in understanding how coordinated behavior arises from the dynamical interaction of an animal's nervous system, its body and its environment. Toward this end, I work on the evolution and analysis of dynamical "nervous systems" for model agents, neuromechanical modeling of animals, biologically-inspired robotics, and dynamical systems approaches to behavior and cognition. More generally, I am interested in computational and theoretical biology, including models of metabolism, gene regulation and development. Recent Publications (Complete publications here) Izquierdo, E.J., Williams, P.L. and Beer, R.D. Agmon, E., Gates, A.J., Churavy, V. and Beer, R.D. Beer, R.D. Beer, R.D. and Williams, P.L. Beer, R.D. Beer, R.D. (2014). Beer, R.D. (2014). Agmon, E. and Beer, R.D. (2014). Izquierdo, E.J., Aguilera, M. and Beer, R.D. (2013). Williams, P. and Beer, R.D. (2013). Izquierdo, E.J. and Beer, R.D. (2013). Software Dynamica
Genetic Algorithm Tutorial Genetic Algorithms in Plain English Introduction The aim of this tutorial is to explain genetic algorithms sufficiently for you to be able to use them in your own projects. This tutorial is designed to be read through twice... so don't worry if little of it makes sense the first time you study it. (A reader, Daniel, has kindly translated this tutorial into German. (Another reader, David Lewin, has translated the tutorial into French. First, a Biology Lesson Every organism has a set of rules, a blueprint so to speak, describing how that organism is built up from the tiny building blocks of life. When two organisms mate they share their genes. Life on earth has evolved to be as it is through the processes of natural selection, recombination and mutation. Once upon a time there lived a species of creatures called Hooters. For a while it looked as though the Hooters may be hunted to extinction, for although they liked to eat the moss they could never tell if an eagle was flying above.
The Foundation for Science and Technology Robots evolve to exploit inadvertent cues Human interaction heavily depends on inadvertent cues: A competitor's sweaty handshake before a negotiation, a girl blushing when introducing herself, or the trace of a smile crossing the face of a poker player all convey important information. Sara Mitri and colleagues at the Laboratory of Intelligent Systems (disclaimer: my former lab) at the EPFL in Switzerland have now shown that it is not just humans who can develop, detect and use inadvertent cues to their advantage (PNAS: "Evolution of Information Suppression in Communicating Robots with Conflicting Interests"). The researchers set up a group of S-bots equipped with omnidirectional cameras and light-emitting rings around their body in a bio-inspired foraging task (see picture above). This is when things became interesting: Due to the limited amount of food, robots now began to compete for resources. I think this research highlights an interesting point: Robots have applicability far beyond engineering. Thanks Sara!
Commission européenne : CORDIS : Nouvelles : Appel à un changement radical dans l'enseignement des sciences Appel à un changement radical dans l'enseignement des sciences [Date: 2007-06-13] Selon le groupe à haut niveau sur l'enseignement des sciences, une approche radicalement nouvelle de l'enseignement des sciences est nécessaire si nous voulons enrayer le déclin de l'intérêt des écoliers européens pour les sciences. Le groupe d'experts a été créé l'année passée par les commissaires européens Janez Potocnik et Jan Figel', respectivement en charge de la recherche et de l'éducation, de la formation, de la culture et de la jeunesse. Il était chargé d'identifier les meilleures pratiques susceptibles d'accroître l'intérêt des jeunes pour les sciences. La recommandation principale du groupe concerne un revirement de l'enseignement des sciences dans les écoles pour passer d'une méthode principalement déductive à une méthode basée sur le questionnement. Les auteurs du rapport demandent une meilleure articulation entre les activités nationales et celles financées au niveau européen.
ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ Devert2012 As a goodie: a bibtex file that reference all my publications. A Study on Scalable Representations for Evolutionary Optimization of Ground Structures Accepted for publication in Evolutionary Computation, to appear. Robustness and the Halting Problem for Multi-Cellular Artificial Ontogeny Accepted for publication in IEEE Transactions on Evolutionary Computation, June 2011. When and why development is needed: generative and developmental systems Accepted for publication at GECCO 2009, Montreal, Québec, Canada, 2009. Artificial Ontogeny for Truss Structure Design Accepted for publication at Workshop on Spatial Computing (SCW), at the second IEEE International Conference on Self-Adaptive and Self-Organizing Systems, Venice, Italy, 2008. Unsupervised Learning of Echo State Networks: A Case Study in Artificial Embryogeny Accepted for publication at EA 2007, Tour, France, October 29-31th 2007. Robust Multi-Cellular Developmental Design
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