By Bill Gunston
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Evolutionary Algorithms (EAs) now supply mature optimization instruments that experience effectively been utilized to many difficulties, from designing antennas to accomplish robots, and supplied many human-competitive effects. In robotics, the mixing of EAs in the engineer’s toolbox made large growth within the final two decades and proposes new the way to tackle demanding difficulties in quite a few setups: modular robotics, swarm robotics, robotics with non-conventional mechanics (e.
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Principles of Modularity, Regularity, and Hierarchy for Scalable Systems. In: Genetic and Evolutionary Computation Conference (GECCO 2004) Workshop on Modularity, regularity and Hierarchy (2004) 63. : Evolutionary robotics for legged machines: from simulation to physical reality. Intelligent Autonomous Systems 9, 9 (2006) 64. : Automatic design and manufacture of robotic life forms. Nature 406(406), 974–978 (2000) 65. : Evolvable systems for space applications. In: International Conference on Space Mission Challenges for Information Technology (2003) 66.
Furthermore, new computational abstractions of evolution may need to be developed that capture the open-ended process through which the products of nature were discovered [8, 10, 16]. Thus significant research lies ahead. Yet these research directions provide a hint of where opportunity may lie.
Stanley Abstract. While at present Evolutionary Robotics (ER) is generally not studied in mainstream robotics, the main idea in this article is that ER has the opportunity to gain relevance by taking seriously its natural inspiration. The chasm that separates the behavior of robots today from the robustness and fluidity of organisms in nature is most naturally addressed by an approach that indeed respects the process through which such organisms originated. Yet the challenge is to identify the elusive missing ingredient that would allow ER to realize its full potential.