@book{
 title = {Coupler-curve synthesis of a planar four-bar mechanism using NSGA-II},
 type = {book},
 year = {2012},
 source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 identifiers = {[object Object]},
 keywords = {Coupler-curve Synthesis,Design of Experiments,Genetic Algorithms,NSGA-II,Optimisation},
 volume = {7673 LNCS},
 id = {760c8465-d32d-3a7e-859a-e904045c3b6b},
 created = {2018-04-26T04:49:31.163Z},
 file_attached = {false},
 profile_id = {0b583732-52d5-33b9-b059-682c26f2d83c},
 last_modified = {2018-04-26T04:49:31.163Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper applies a genetic algorithm-based optimisation procedure, namely, NSGA-II, to the problem of synthesis of a four-bar mechanism. The internal parameters of are tuned using a Design of Experiments (DoE) procedure to enhance the quality of the final results. Constraints are handled through a penalty formulation. Further, a scaling function is introduced, which transforms the penalty terms in a manner that leads to faster convergence of the solutions. The theoretical developments are illustrated via applications to two well-studied problems in the domain of coupler-curve synthesis. A comparison of the results vis-a-vis existing ones shows that the proposed enhancements of the basic scheme of deliver promising improvements in terms of accuracy, and rate of convergence of the solutions. © 2012 Springer-Verlag.},
 bibtype = {book},
 author = {Badduri, J. and Srivatsan, R.A. and Kumar, G.S. and Bandyopadhyay, S.}
}

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