SNDL-MOEA: "Stored Non-Domination Level MOEA"Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (2007, London, England)
AbstractThere exist a number of high-performance Multi-Objective Evolutionary Algorithms (MOEAs) for solving Multi-Objective Optimization (MOO) problems; two of the best are NSGA-II and epsilon-MOEA. However, they lack an archive population sorted into levels of non-domination, making them unsuitable for construction problems where some type of backtracking to earlier intermediate solutions is required. In this paper we introduce our Stored Non-Domination Level (SNDL) MOEA for solving such construction problems. SNDL-MOEA combines some of the best features of NSGA-II and epsilon-MOEA with the ability to store and recall intermediate solutions necessary for construction problems. We present results for applying SNDL-MOEA to the Tight Single Change Covering Design (TSCCD) construction problem, demonstrating its applicability. Furthermore, we show with a detailed performance comparison between SNDL-MOEA, NSGA-II, and epsilon-MOEA on two standard test series that SNDL-MOEA is capable of outperforming NSGA-II and is competitive with epsilon-MOEA.
Meeting Name9th Annual Conference on Genetic and Evolutionary Computation (2007: July 7-11, London, England)
Keywords and Phrases
- Constructive Problem Solving,
- Evolutionary Multiobjective Optimization,
- Pareto Optimality
Document TypeArticle - Conference proceedings
Rights© 2007 Association for Computing Machinery (ACM), All rights reserved.
Citation InformationMatt D. Johnson, Daniel R. Tauritz and Ralph W. Wilkerson. "SNDL-MOEA: "Stored Non-Domination Level MOEA"" Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (2007, London, England) (2007)
Available at: http://works.bepress.com/daniel-tauritz/62/