March 1, 2014

Paper: Efficient Multi-Objective Optimization Using 2-Population Cooperative Coevolution - EUROCAST 2013

This paper was prepared for the EUROCAST 2013 conference and contains a proof-of-concept for DECMO - a simple coevolutionary multi-objective optimization algorithm that, on several problems is successfully able to adopt the search behavior of the most successful strategy it incorporates. This line of research was continued and fully fleshed out in DECMO2 - a subsequent algorithm I developed for my PhD. Here is the abstract our EUROCAST 2013 submission:
We propose a 2-population cooperative coevolutionary optimization method that can efficiently solve multi-objective optimization problems as it successfully combines positive traits from classic multi-objective evolutionary algorithms and from newer optimization approaches that explore the concept of differential evolution. A key part of the algorithm lies in the proposed dual fitness sharing mechanism that is able to smoothly transfer information between the two coevolved populations without negatively impacting the independent evolutionary process behavior that characterizes each population.
You can download a preliminary version of the paper by clicking here or from my Downloads box (Efficient Multi-Objective Optimization Using 2-Population Cooperative Coevolution - EUROCAST 2013.pdf). The same preliminary draft of the document can be previewed at the bottom of this post. The original publication is available at

For citations please use the following BibTeX reference:

  author = {Alexandru-Ciprian Z\u{a}voianu and Edwin Lughofer and Wolfgang Amrhein and Erich Peter Klement},
  title = {Efficient Multi-Objective Optimization Using 2-Population Cooperative Coevolution},
  booktitle = {Computer Aided Systems Theory - EUROCAST 2013},
  publisher = {Springer Berlin / Heidelberg},
  year = {2013},
  editor = {Moreno-Diaz, Roberto and Pichler, Franz and Quesada-Arencibia, Alexis},
  volume = {8111},
  series = {Lecture Notes in Computer Science},
  pages = {251-258},
  doi = {10.1007/978-3-642-53856-8_32},

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