Phylogenetic Differential Evolution

This paper presents a new technique for optimizing binary problems with building blocks. The authors have developed a different approach to existing Estimation of Distribution Algorithms (EDAs). Our technique, called Phylogenetic Differential Evolution (PhyDE), combines the Phylogenetic Algorithm and the Differential Evolution Algorithm. The first one is employed to identify the building blocks and to generate metavariables. The second one is used to find the best instance of each metavariable. In contrast to existing EDAs that identify the related variables at each iteration, the presented technique finds the related variables only once at the beginning of the algorithm, and not through the generations. This paper shows that the proposed technique is more efficient than the well known EDA called Extended Compact Genetic Algorithm (ECGA), especially for large-scale systems which are commonly found in real world problems.



  • A hybrid global optimization algorithm for discrete global optimization
  • Linkage Learning via Probabilistic Modeling
  • Tested five mutation strategies
  • Tested in: deceptive trap problems
  • Compared to: Extended Compact Genetic Algorithm ( ECGA )


added-at = {2011-07-12T00:00:00.000+0200},
author = {Melo, Vinícius Veloso de and Vargas, Danilo Vasconcellos and Crocomo, Marcio Kassouf and Delbem, Alexandre Claudio Botazzo},
biburl = {http://www.bibsonomy.org/bibtex/228d09469630842d89c70f60d0f6f9542/dblp},
ee = {http://dx.doi.org/10.4018/jncr.2011010102},
interhash = {91fd313c446818438129313f607d2911},
intrahash = {28d09469630842d89c70f60d0f6f9542},
journal = {IJNCR},
keywords = {dblp},
number = 1,
pages = {21-38},
timestamp = {2011-07-12T00:00:00.000+0200},
title = {Phylogenetic Differential Evolution.},
url = {http://dblp.uni-trier.de/db/journals/ijncr/ijncr2.html#MeloVC11},
volume = 2,
year = 2011