000191284 001__ 191284
000191284 005__ 20190316235755.0
000191284 0247_ $$2doi$$a10.1371/journal.pone.0086831
000191284 02470 $$2ISI$$a000330570000072
000191284 037__ $$aARTICLE
000191284 245__ $$aArtificial Evolution by Viability Rather Than Competition
000191284 269__ $$a2014
000191284 260__ $$aSan Francisco$$bPublic Library of Science$$c2014
000191284 300__ $$a12
000191284 336__ $$aJournal Articles
000191284 520__ $$aEvolutionary algorithms are widespread heuristic methods inspired by natural evolution to solve difficult problems for which analytical approaches are not suitable. In many domains experimenters are not only interested in discovering optimal solutions, but also in finding the largest number of different solutions satisfying minimal requirements. However, the formulation of an effective performance measure describing these requirements, also known as fitness function, represents a major challenge. The difficulty of combining and weighting multiple problem objectives and constraints of possibly varying nature and scale into a single fitness function often leads to unsatisfactory solutions. Furthermore, selective reproduction of the fittest solutions, which is inspired by competition-based selection in nature, leads to loss of diversity within the evolving population and premature convergence of the algorithm, hindering the discovery of many different solutions. Here we present an alternative abstraction of artificial evolution, which does not require the formulation of a composite fitness function. Inspired from viability theory in dynamical systems, natural evolution and ethology, the proposed method puts emphasis on the elimination of individuals that do not meet a set of changing criteria, which are defined on the problem objectives and constraints. Experimental results show that the proposed method maintains higher diversity in the evolving population and generates more unique solutions when compared to classical competition-based evolutionary algorithms. Our findings suggest that incorporating viability principles into evolutionary algorithms can significantly improve the applicability and effectiveness of evolutionary methods to numerous complex problems of science and engineering, ranging from protein structure prediction to aircraft wing design.
000191284 6531_ $$aArtificial Evolution
000191284 6531_ $$aStochastic Optimisation
000191284 6531_ $$aViability Evolution
000191284 6531_ $$aConstraint Handling
000191284 6531_ $$aConstrained Optimisation
000191284 6531_ $$aEvolutionary Robotics
000191284 700__ $$0244468$$aMaesani, Andrea$$g195419
000191284 700__ $$0243234$$aFernando, Pradeep Ruben$$g201481
000191284 700__ $$0240742$$aFloreano, Dario$$g111729
000191284 773__ $$j9$$k1$$qe86831$$tPLOS One
000191284 8564_ $$s1153892$$uhttps://infoscience.epfl.ch/record/191284/files/journal.pone.0086831.pdf$$yPublisher's version$$zPublisher's version
000191284 909C0 $$0252161$$pLIS$$xU10370
000191284 909CO $$ooai:infoscience.tind.io:191284$$pSTI$$particle$$qGLOBAL_SET
000191284 917Z8 $$x195419
000191284 917Z8 $$x195419
000191284 917Z8 $$x195419
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000191284 937__ $$aEPFL-ARTICLE-191284
000191284 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000191284 980__ $$aARTICLE