June 13, 2013

Paper: On the Performance of Master-Slave Parallelization Methods for Multi-Objective Evolutionary Algorithms - ICAISC 2013

This paper was prepared for the ICAISC 2013 conference and it is related to my current research project that has the general aim of enhancing currently available Evolutionary Computation methods employed for the multi-objective optimization of problems that rely on a very time-intensive fitness evaluation functionsHere is the abstract of the article:
This paper is focused on a comparative analysis of the performance of two master-slave parallelization methods, the basic generational scheme and the steady-state asynchronous scheme. Both can be used to improve the convergence speed of multi-objective evolutionary algorithms (MOEAs) that rely on time-intensive fitness evaluation functions. The importance of this work stems from the fact that a correct choice for one or the other parallelization method can lead to considerable speed improvements with regards to the overall duration of the optimization. Our main aim is to provide practitioners of MOEAs with a simple but effective method of deciding which master-slave parallelization option is better when dealing with a time-constrained optimization process.
You can download a preliminary version of the paper by clicking here or from my Downloads box (On the Performance of Master-Slave Parallelization Methods for MOEAs - ICAISC 2013.pdf). The same preliminary draft of the document can be previewed at the bottom of this post. The original publication is available at www.springerlink.com.

For citations please use the following BibTeX reference:

  author = {Alexandru-Ciprian Z\u{a}voianu and Edwin Lughofer and Werner Koppelst\"{a}tter and G\"{u}nther Weidenholzer and Wolfgang Amrhein and Erich Peter Klement},
  title = {On the Performance of Master-Slave Parallelization Methods for Multi-Objective Evolutionary Algorithms},
  booktitle = {Artificial Intelligence and Soft Computing},
  publisher = {Springer Berlin Heidelberg},
  year = {2013},
  editor = {Laszek Rutkowski et al.},
  volume = {7895},
  series = {Lecture Notes in Artificial Intelligence},
  pages = {122-134},
  doi = {10.1007/978-3-642-38610-7_12}

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