000220676 001__ 220676
000220676 005__ 20190317000512.0
000220676 0247_ $$2doi$$a10.1145/2908961.2931675
000220676 02470 $$2ISI$$a000383741800142
000220676 037__ $$aCONF
000220676 245__ $$aGaining Insight into Quality Diversity
000220676 269__ $$a2016
000220676 260__ $$aNew York, New York, USA$$bACM Press$$c2016
000220676 300__ $$a4
000220676 336__ $$aConference Papers
000220676 520__ $$aRecently there has been a growing movement of researchers that believes innovation and novelty creation, rather than pure optimization, are the true strengths of evolutionary algorithms relative to other forms of machine learning. This idea also provides one possible explanation for why evolutionary processes may exist in nervous systems on top of other forms of learning. One particularly exciting corollary of this, is that evolutionary algorithms may be used to produce what Pugh et al have dubbed Quality Diversity (QD): as many as possible different solutions (according to some characterization), which are all as fit as possible. While the notion of QD implies choosing the dimensions on which to measure diversity and performance, we propose that it may be possible (and desirable) to free the evolutionary process from requiring defining these dimensions. Toward that aim, we seek to understand more about QD in general by investigating how algorithms informed by different measures of diversity (or none at all) create QD, when that QD is measured in a diversity of ways.
000220676 6531_ $$aEvolutionary Computation
000220676 6531_ $$aNon-objective search
000220676 6531_ $$aBehavior Characterization
000220676 6531_ $$aRobotics
000220676 6531_ $$aQuality Diversity
000220676 6531_ $$aNeuroevolu- tion
000220676 700__ $$aAuerbach, Joshua E.
000220676 700__ $$0247888$$aIacca, Giovanni$$g242288
000220676 700__ $$0240742$$aFloreano, Dario$$g111729
000220676 7112_ $$aGECCO '16$$cDenver, Colorado, USA$$d20-24 07 2016
000220676 773__ $$q1061-1064$$tProceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion - GECCO '16 Companion
000220676 8564_ $$s1213536$$uhttps://infoscience.epfl.ch/record/220676/files/p1061-auerbach.pdf$$yPublisher's version$$zPublisher's version
000220676 909C0 $$0252161$$pLIS$$xU10370
000220676 909CO $$ooai:infoscience.tind.io:220676$$pconf$$pSTI$$qGLOBAL_SET
000220676 917Z8 $$x239571
000220676 937__ $$aEPFL-CONF-220676
000220676 973__ $$aEPFL$$rNON-REVIEWED$$sPUBLISHED
000220676 980__ $$aCONF