@book{
 title = {Hybrid evolutionary algorithm for solving global optimization problems},
 type = {book},
 year = {2009},
 source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 identifiers = {[object Object]},
 keywords = {Differential Evolution,Evolutionary Programming,Global Optimization,Hybrid Algorithm},
 volume = {5572 LNAI},
 id = {4b46cbf9-9d15-3211-bc8d-afacdc6ffdbc},
 created = {2018-02-20T21:30:51.776Z},
 file_attached = {false},
 profile_id = {54318c42-96bc-3aaf-a27d-ca643ea40072},
 last_modified = {2018-02-20T21:30:51.776Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Differential Evolution (DE) is a novel evolutionary approach capable of handling non-differentiable, non-linear and multi-modal objective functions. DE has been consistently ranked as one of the best search algorithm for solving global optimization problems in several case studies. This paper presents a simple and modified hybridized Differential Evolution algorithm for solving global optimization problems. The proposed algorithm is a hybrid of Differential Evolution (DE) and Evolutionary Programming (EP). Based on the generation of initial population, three versions are proposed. Besides using the uniform distribution (U-MDE), the Gaussian distribution (G-MDE) and Sobol sequence (S-MDE) are also used for generating the initial population. Empirical results show that the proposed versions are quite competent for solving the considered test functions. © 2009 Springer Berlin Heidelberg.},
 bibtype = {book},
 author = {Thangaraj, R. and Pant, M. and Abraham, A. and Badr, Y.}
}

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