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The Influence of Data Representation in the Performance of Evolutionary Algorithms |
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Written by Administrator
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Thursday, 16 September 2010 11:12 |
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Title: The Influence of Data Representation in the Performance of Evolutionary Algorithms Authors: Enrique Alba and Edgardo Ferretti and Juan M. Molina Source: Proceedings of the 11th international conference on Computer aided systems theory Abstract: In this paper we study the differences in performance among different implementations, in Java, of the data structures used in an evolutionary algorithm (EA). Typical studies on EAs performance deal only with different abstract representations of the data and pay no attention to the fact that each data representation has to be implemented in a concrete programming language which, in general, offers several possibilities, with differences in time consumed, that may be worthy of consideration. DOI: http://portal.acm.org/citation.cfm?id=1783139
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Last Updated on Thursday, 16 September 2010 11:30 |
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Single and multi-objective genetic operators in object-oriented conceptual software design |
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Written by Administrator
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Thursday, 11 September 2008 11:25 |
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Title: Single and multi-objective genetic operators in object-oriented conceptual software design Authors: Simons, CL (Simons, C. L.); Parmee, IC (Parmee, I. C.) Source: GECCO 2006: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2 Pages: 1957-1958 Published: 2006 Abstract: This poster paper investigates the potential of single and multiobjective genetic operators with an object-oriented conceptual design space. Using cohesion as an objective fitness function, genetic operators inspired by genetic algorithms and evolutionary programming are compared against a simple case study. Also, using both cohesion and coupling as objective fitness functions, multi-objective genetic operators inspired by a non-dominated sorting algorithm have been developed. Cohesion and coupling values achieved are similar to human performed designs and a large number and variety of optimal solutions are arrived at, which could not have been produced by the human software engineer. We conclude that this mass of optimal design variants offers significant potential for design support when integrated with user-centric, computationally intelligent tools. DOI: http://doi.acm.org/10.1145/1143997.1144324 |
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