Testing parallelization paradigms for MOEAs

In this paper, we report on our investigation of factors affecting the performance of various parallelization paradigms for multiobjective evolutionary algorithms. Different parallelization paradigms emphasize separate development of sub-populations versus communication and coordination between sub-populations to greater or lesser degrees. We hypothesized that the characteristics of a particular problem will favour some paradigms over others. We tested this hypothesis by creating variations on test problems with different characteristics, and testing the performance of different paradigms in a cluster environment. Categories and Subject Descriptors

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Sadeesha Gamhewa, Philip Hingston
• Keywords: GECCO 2008, Multiobjective Evolutionary Algorithms, Optimization, Parallelization Paradigms, Various Parallelization Paradigms