Nick Cheney
Skip Abstract Section
Abstract
In 1994, Karl Sims' evolved virtual creatures showed the potential of evolutionary algorithms to produce natural, complex morphologies and behaviors [30]. One might assume that nearly 20 years of improvements in computational speed and evolutionary algorithms would produce far more impressive organisms, yet the creatures evolved in the field of artificial life today are not obviously more complex, natural, or intelligent. Fig. 2 demonstrates an example of similar complexity in robots evolved 17 years apart.
- J. E. Auerbach and J. C. Bongard. Dynamic resolution in the co-evolution of morphology and control. In Artificial Life XII: Proc. of the 12th International Conf. on the Simulation and Synthesis of Living Systems, 2010.Google Scholar
- J. E. Auerbach and J. C. Bongard. Evolving CPPNs to grow three-dimensional physical structures. In Proc. of the Genetic & Evolutionary Computation Conf., pages 627--634. ACM, 2010. Google ScholarDigital Library
- J. E. Auerbach and J. C. Bongard. On the relationship between environmental and mechanical complexity in evolved robots. In Proc. of the Artificial Life Conf., pages 309--316, 2012.Google ScholarCross Ref
- J. E. Auerbach and J. C. Bongard. On the relationship between environmental and morphological complexity in evolved robots. In Proc. of the Genetic & Evolutionary Computation Conf., pages 521--528. ACM, 2012. Google ScholarDigital Library
- J. Bongard. Evolving modular genetic regulatory networks. In Evolutionary Computation, 2002. Proc. of the 2002 Congress on, volume 2, pages 1872--1877. IEEE, 2002. Google ScholarDigital Library
- J. Clune, B. Beckmann, C. Ofria, and R. Pennock. Evolving coordinated quadruped gaits with the HyperNEAT generative encoding. In Proceedings of the IEEE Congress on Evolutionary Computation, pages 2764--2771, 2009. Google ScholarDigital Library
- J. Clune and H. Lipson. Evolving three-dimensional objects with a generative encoding inspired by developmental biology. In Proc. of the European Conf. on Artificial Life, pages 144--148, 2011.Google Scholar
- J. Clune, J.-B. Mouret, and H. Lipson. The evolutionary origins of modularity. Proc. of the Royal Society B, 280(20122863), 2013.Google ScholarCross Ref
- J. Clune, K. O. Stanley, R. T. Pennock, and C. Ofria. On the performance of indirect encoding across the continuum of regularity. IEEE Trans. on Evolutionary Computation, 15(4):346--367, 2011. Google ScholarDigital Library
- J. Gauci and K. O. Stanley. Generating large-scale neural networks through discovering geometric regularities. In Proc. of the Genetic & Evolutionary Computation Conf., pages 997--1004. ACM, 2007. Google ScholarDigital Library
- J. D. Hiller and H. Lipson. Multi-material topological optimization of structures and mechanisms. In Proc. of the Genetic & Evolutionary Computation Conf., pages 1521--1528. ACM, 2009. Google ScholarDigital Library
- J. D. Hiller and H. Lipson. Evolving amorphous robots. Artificial Life XII, pages 717--724, 2010.Google Scholar
- J. D. Hiller and H. Lipson. Automatic design and manufacture of soft robots. IEEE Trans. on Robotics, 28(2):457--466, 2012.Google ScholarDigital Library
- J. D. Hiller and H. Lipson. Dynamic simulation of soft heterogeneous objects. ArXiv:1212.2845, 2012.Google Scholar
- S. Hirose and Y. Umetani. The development of soft gripper for the versatile robot hand. Mechanism and Machine Theory, 13(3):351--359, 1978.Google ScholarCross Ref
- G. S. Hornby, H. Lipson, and J. B. Pollack. Generative representations for the automated design of modular physical robots. IEEE Trans. on Robotics and Automation, 19(4):703--719, 2003.Google ScholarCross Ref
- G. S. Hornby and J. B. Pollack. Evolving L-systems to generate virtual creatures. Computers & Graphics, 25(6):1041--1048, 2001.Google ScholarCross Ref
- M. Joachimczak and B. Wróbel. Coevolution of morphology and control of soft-bodied multicellular animats. In Proc. of the Genetic & Evolutionary Computation Conf., pages 561--568. ACM, 2012. Google ScholarDigital Library
- M. Komosinski and A. Rotaru-Varga. Comparison of different genotype encodings for simulated three dimensional agents. Artificial Life, 7(4):395--418, 2001. Google ScholarDigital Library
- S. Lee, J. Yosinski, K. Glette, H. Lipson, and J. Clune. Evolving gaits for physical robots with the HyperNEAT generative encoding: the benefits of simulation., 2013.Google Scholar
- J. Lehman and K. O. Stanley. Evolving a diversity of virtual creatures through novelty search and local competition. In Proc. of the Genetic & Evolutionary Computation Conf., pages 211--218. ACM, 2011. Google ScholarDigital Library
- H. Lipson and J. B. Pollack. Automatic design and manufacture of robotic lifeforms. Nature, 406(6799):974--978, 2000.Google ScholarCross Ref
- W. E. Lorensen and H. E. Cline. Marching cubes: A high resolution 3D surface construction algorithm. In ACM Siggraph, volume 21, pages 163--169, 1987. Google ScholarDigital Library
- J.-B. Mouret and J. Clune. An algorithm to create phenotype-fitness maps. Proc. of the Artificial Life Conf., pages 593--594, 2012.Google Scholar
- S. Nolfi, D. Floreano, O. Miglino, and F. Mondada. How to evolve autonomous robots: Different approaches in evolutionary robotics. In Artificial Life IV, pages 190--197. Cambridge, MA: MIT Press, 1994.Google Scholar
- R. Pfeifer and J. C. Bongard. How the body shapes the way we think: a new view of intelligence. MIT press, 2006. Google ScholarDigital Library
- J. Rieffel, F. Saunders, S. Nadimpalli, H. Zhou, S. Hassoun, J. Rife, and B. Trimmer. Evolving soft robotic locomotion in PhysX. In Proc. of the Genetic & Evolutionary Computation Conf., pages 2499--2504. ACM, 2009. Google ScholarDigital Library
- S. Sanan, J. Moidel, and C. G. Atkeson. A continuum approach to safe robots for physical human interaction. In Int'l Symposium on Quality of Life Technology, 2011.Google Scholar
- J. Secretan, N. Beato, D. B. D'Ambrosio, A. Rodriguez, A. Campbell, and K. O. Stanley. Picbreeder: evolving pictures collaboratively online. In Proc. of the 26th SIGCHI Conf. on Human Factors in Computing Systems, pages 1759--1768. ACM, 2008. Google ScholarDigital Library
- K. Sims. Evolving virtual creatures. In Proc. of the 21st Annual Conf. on Computer Graphics and Interactive Techniques, pages 15--22. ACM, 1994. Google ScholarDigital Library
- K. O. Stanley. Compositional pattern producing networks: A novel abstraction of development. Genetic Programming and Evolvable Machines, 8(2):131--162, 2007. Google ScholarDigital Library
- K. O. Stanley, D. B. D'Ambrosio, and J. Gauci. A hypercube-based encoding for evolving large-scale neural networks. Artificial Life, 15(2):185--212, 2009. Google ScholarDigital Library
- K. O. Stanley and R. Miikkulainen. Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2):99--127, 2002. Google ScholarDigital Library
- K. O. Stanley and R. Miikkulainen. A taxonomy for artificial embryogeny. Artificial Life, 9(2):93--130, 2003. Google ScholarDigital Library
- D. Trivedi, C. Rahn, W. Kier, and I. Walker. Soft robotics: Biological inspiration, state of the art, and future research. Applied Bionics and Biomechanics, 5(3):99--117, 2008. Google ScholarDigital Library
- P. Verbancsics and K. O. Stanley. Constraining connectivity to encourage modularity in HyperNEAT. In Proceedings of the 13th annual conference on Genetic and evolutionary computation, pages 1483--1490. ACM, 2011. Google ScholarDigital Library
- M. Wall. Galib: A c++ library of genetic algorithm components. Mechanical Engineering Department, Massachusetts Institute of Technology, 87, 1996.Google Scholar
- B. G. Woolley and K. O. Stanley. On the deleterious effects of a priori objectives on evolution and representation. In Proc. of the Genetic and Evolutionary Computation Conf., pages 957--964. ACM, 2011. Google ScholarDigital Library
Recommendations
Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding
GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computationIn 1994 Karl Sims showed that computational evolution can produce interesting morphologies that resemble natural organisms. Despite nearly two decades of work since, evolved morphologies are not obviously more complex or natural, and the field seems to ...
Evolved open-endedness, not open-ended evolution
Open-endedness is often considered a prerequisite property of the whole evolutionary system and its dynamical behaviors. In the actual history of evolution on Earth, however, there are many examples showing that open-endedness is rather a consequence of ...
Evolvability in grammatical evolution
GECCO '17: Proceedings of the Genetic and Evolutionary Computation ConferenceEvolvability is a measure of the ability of an Evolutionary Algorithm (EA) to improve the fitness of an individual when applying a genetic operator. Other than the specific problem, many aspects of the EA may impact on the evolvability most notably the ...
Comments