Quality Diversity: A New Frontier for Evolutionary Computation
Tóm tắt
Từ khóa
Tài liệu tham khảo
Bäck, 1997, Evolutionary computation: comments on the history and current state, IEEE Trans. Evol. Comput., 1, 3, 10.1109/4235.585888
Bedau, 2008, “The arrow of complexity hypothesis (abstract),”, 750
Bishop, 2006, Pattern Recognition and Machine Learning
Boden, 2006, Mind as Machine: A History of Cognitive Science
Bongard, 2002, “Evolving modular genetic regulatory networks,”, Proceedings of the 2002 Congress on Evolutionary Computation, Honolulu, 10.1109/CEC.2002.1004528
Cliff, 1993, Explorations in evolutionary robotics, Adapt. Behav., 2, 73, 10.1177/105971239300200104
Cully, 2013, “Behavioral repertoire learning in robotics,”, 175
De Jong, 2002, Evolutionary Computation: A Unified Perspective
Deb, 2002, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., 6, 182, 10.1109/4235.996017
Doncieux, 2015, Evolutionary robotics: what, why, and where to, Front. Robot. AI, 2, 4, 10.3389/frobt.2015.00004
Doucette, 2010, “Novelty-based fitness: an evaluation under the santa fe trail,”, 50
Fogel, 1966, Artificial Intelligence Through Simulated Evolution
Goldberg, 1989, Genetic Algorithms in Search, Optimization and Machine Learning
Goldberg, 1987, ‘‘Genetic algorithms with sharing for multimodal function optimization,’’, 41
Goldsby, 2010, “Automatically discovering properties that specify the latent behavior of UML models,”, Model Driven Engineering Languages and Systems, 316, 10.1007/978-3-642-16145-2_22
Gomes, 2013, “Generic behaviour similarity measures for evolutionary swarm robotics,”, 199
Gomes, 2015, “Devising effective novelty search algorithms: a comprehensive empirical study,”, 943
Gomes, 2013, Evolution of swarm robotics systems with novelty search, Swarm Intell., 7, 115, 10.1007/s11721-013-0081-z
Graening, 2010, “Towards directed open-ended search by a novelty guided evolution strategy,”, Parallel Problem Solving from Nature – PPSN XI. Vol. 6239 of Lecture Notes in Computer Science, Krakow, 71
Green, 2003–2006, SharpNEAT Homepage
Holland, 1975, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Hornby, 2001, “The advantages of generative grammatical encodings for physical design,”, Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, 10.1109/CEC.2001.934446
Hornby, 2002, Creating high-level components with a generative representation for body-brain evolution, Artif. Life, 8, 10.1162/106454602320991837
Kistemaker, 2011, “Critical factors in the performance of novelty search,”, 965
Krcah, 2010, “Solving deceptive tasks in robot body-brain co-evolution by searching for behavioral novelty,”, 284
Lehman, 2016, “Creative generation of 3D objects with deep learning and innovation engines,”
Lehman, 2008, “Exploiting open-endedness to solve problems through the search for novelty,”
Lehman, 2010, “Revising the evolutionary computation abstraction: minimal criteria novelty search,”, 103
Lehman, 2011a, Abandoning objectives: evolution through the search for novelty alone, Evol. Comput., 19, 189, 10.1162/EVCO_a_00025
Lehman, 2011b, “Evolving a diversity of virtual creatures through novelty search and local competition,”, 211
Lehman, 2013, Evolvability is inevitable: increasing evolvability without the pressure to adapt, PLoS ONE, 8, e62186, 10.1371/journal.pone.0062186
Liapis, 2013a, “Transforming exploratory creativity with delenox,”
Liapis, 2013b, “Enhancements to constrained novelty search: two-population novelty search for generating game content,”, 343
Martinez, 2013, “Searching for novel regression functions,”, 2013 IEEE Congress on Evolutionary Computation (CEC), Cancun, 16, 10.1109/CEC.2013.6557548
Mitchell, 1997, Machine Learning
Morse, 2013, “Single-unit pattern generators for quadruped locomotion,”, 719
Mouret, 2011, “Novelty-based multiobjectivization,”, New Horizons in Evolutionary Robotics, 139, 10.1007/978-3-642-18272-3_10
Mouret, 2015, Illuminating search spaces by mapping elites
Mouret, 2009, “Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity,”, Proceedings of the IEEE Congress on Evolutionary Computation (CEC-2009), Trondheim, 1161, 10.1109/CEC.2009.4983077
Mouret, 2012, Encouraging behavioral diversity in evolutionary robotics: an empirical study, Evol. Comput., 20, 91, 10.1162/EVCO_a_00048
Naredo, 2013, “Searching for novel clustering programs,”, 1093
Nguyen, 2015a, “Deep neural networks are easily fooled: high confidence predictions for unrecognizable images,”, 10.1109/CVPR.2015.7298640
Nguyen, 2015b, “Innovation engines: automated creativity and improved stochastic optimization via deep learning,”, 10.1145/2739480.2754703
Nolfi, 2000, Evolutionary Robotics
Risi, 2011, Evolving plastic neural networks with novelty search, Adapt. Behav., 18, 470, 10.1177/1059712310379923
Risi, 2013, “Confronting the challenge of learning a flexible neural controller for a diversity of morphologies,”, 10.1145/2463372.2463397
Risi, 2009, “How novelty search escapes the deceptive trap of learning to learn,”, 10.1145/1569901.1569923
Rumelhart, 1986, “Learning internal representations by error propagation,”, 318
Schwefel, 1993, Evolution and Optimum Seeking: The Sixth Generation
Simon, 1957, Models of Man: Social and Rational – Mathematical Essays on Rational Human Behavior in a Social Setting
Soltoggio, 2009, “Novelty of behaviour as a basis for the neuro-evolution of operant reward learning,”, 169
Standish, 2003, Open-ended artificial evolution, Int. J. Comput. Intell. Appl., 3, 167, 10.1142/S1469026803000914
Stanley, 2007, “Compositional pattern producing networks: a novel abstraction of development,”, Genetic Programming and Evolvable Machines Special Issue on Developmental Systems, 131
Stanley, 2011, “Why evolutionary robotics will matter,”, New Horizons in Evolutionary Robotics, 37, 10.1007/978-3-642-18272-3_3
Stanley, 2002, Evolving neural networks through augmenting topologies, Evol. Comput., 10, 99, 10.1162/106365602320169811
Szerlip, 2013, “Indirectly encoded sodarace for artificial life,”, 218
Szerlip, 2015, “Unsupervised feature learning through divergent discriminative feature accumulation,”, 10.1609/aaai.v29i1.9601
Trujillo, 2008, “Discovering several robot behaviors through speciation,”, Applications of Evolutionary Computing, 164, 10.1007/978-3-540-78761-7_17
Trujillo, 2011, Speciation in behavioral space for evolutionary robotics, J. Intell. Robot. Syst., 64, 323, 10.1007/s10846-011-9542-z
Velez, 2014, “Novelty search creates robots with general skills for exploration,”, 737
Woolley, 2011, “On the deleterious effects of a priori objectives on evolution and representation,”, 957