Journal Title
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Publisher
Springer, Berlin, Heidelberg
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Authors: P Amato M Farina
Publish Date: 2005
Volume: , Issue: , Pages: 113-125
Abstract
Several important applications require a timedependent online in which either the objective function or the problem parameters or both vary with time Several studies are available in the literature about the use of genetic algorithms for time dependent fitness landscape in singleobjective optimization problems But when dynamic multiobjective optimization is concerned very few studies can be found Taking inspiration from Artificial Life ALife a strategy is proposed ensuring the approximation of Paretooptimal set and front in case of unpredictable parameters changes It is essentially an ALifeinspired evolutionary algorithm for variable fitness landscape search We describe the algorithm and test it on some test cases
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