Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
An Active Elitism Mechanism for Multi-objective Evolutionary Algorithms
Engin Ufuk Ergul
Pages - 150 - 166     |    Revised - 01-09-2011     |    Published - 05-10-2011
Volume - 2   Issue - 4    |    Publication Date - September / October 2011  Table of Contents
Evolutionary Algorithms, Active Elitism, Multi-objective Optimization
Classical (or passive) elitism mechanisms in the literature have a holding/sending back structure. In the active elitism mechanism proposed in this paper, a set of elite individuals is excited by genetic operators (crossover/mutation) in archive in the hope of generating better and more diverse individuals than themselves. If a set of excited elites are any better than originals, then archive can be viewed as a place of active solution provider rather than a static storage place. The main motivation behind this approach is that elite individuals are inherently the closest individuals to the solution (of any optimization problem on hand) and exciting those individuals can likely generate more significant outcomes than a far away one. The proposed active elitism mechanism is embedded into well-known multi-objective SPEA and SPEA2 methods (named ACE_SPEA and ACE_SPEA2 respectively) and compared to the original methods using four benchmarks. The active elitist versions of SPEA and SPEA2 maintain better spread and convergence properties than the original methods. The proposed active elitism mechanism can easily be integrated into existing multi-objective evolutionary algorithms.
CITED BY (1)  
1 Maheta, H. H., & Dabhi, V. K. (2014, February). An improved SPEA2 Multi objective algorithm with non dominated elitism and Generational Crossover. In Issues and Challenges in Intelligent Computing Techniques (ICICT), 2014 International Conference on (pp. 75-82). IEEE.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 C.A. Coello Coello. “A Comprehensive Survey of Evolutionary-Based Multi-objective Optimization Techniques”. Knowledge and Information Systems, Vol.1, Issue.3, pp. 269- 308, 1999.
2 K. Deb. Multiobjective Optimization Using Evolutionary Algorithms. Chichester, U.K: Wiley, 2001.
3 Y. Zhenyu, K. Lishan, B. Mckay, F. Penghui. “SEEA for Multi-Objective Optimization: Reinforcing Elitist MOEA through Multi-Parent Crossover, Steady Elimination and Swarm Hill Climbing”. Proceedings of the 4th Asia-Pasific Conference on Simulated Evolution and Learning, 2002, pp. 21-26.
4 E. Zitzler. “Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications”. Ph.D Thesis, Swiss Federal Institute of Technology, Zurich, Switzerland, 1999.
5 E. Zitzler, L. Thiele. “Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach”. IEEE Trans. on Evolutionary Computation, Vol.3, No. 4, pp. 257-271, 1999.
6 G. Rudolph. “Evolutionary Search Under Partially Ordered Fitness Sets”. Proceedings of the International Symposium on Information Science Innovations in Engineering of Natural and Artificial Intelligent Systems, 2001, pp. 818-822.
7 G. Rudolph. “Convergence of Evolutionary Algorithms in General Search Spaces”. Proceedings of the Third IEEE Conference on Evolutionary Computation, 1996, pp. 50-54.
8 E. Zitzler, M. Laumanns, L. Thiele. “SPEA2: Improving the Strength Pareto Evolutionary Algorithm”.TIK-Report 103, Swiss Federal Institute of Technology, Zurich, Switzerland, May 2001.
9 K. Deb, A. Pratap, S. Agarwal, T. Meyarivan. “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II”. IEEE Transactions on Evolutionary Computation, Vol.6, No.2, April 2002.
10 A. Osyczka, S. Kundu. “A New Method to Solve Generalized Multicriteria Optimization Problems Using The Simple Genetic Algorithm”. Structural Optimization,10(2), pp. 94- 99,1995.
11 D.V. Veldhuizen. “Multiobjective Evolutionary Algorithms: Classifications, Analyses and New Innovaitons”. Ph.D. Thesis, Dayton, OH: Air Force Institute of Technology, 1999.
12 E. Zitzler, M. Laumanns, L. Thiele. “SPEA2: Improving the Strength Pareto Evolutionary Algorithm”. TIK-Report 103, Swiss Federal Institute of Technology, Zurich, May 2001.
13 T. Talaslioglu. “Multi-objective Design Optimization of Grillage Systems According to LRFDAISC”. Advances in Civil Engineering, 2011. (In Press).
14 H. Usta. “Effects Of Mutation, Crossover And Improvements in Elitism Mechanism In Genetic Algorithms”. MSc Thesis (in Turkish), Ondokuz Mayis University, 2007.
15 E. Zitzler , L. Thiele, M. Laumanns, C.M. Fonseca, and V.G. Fonseca. “Performance Assesments of Multiobjective Optimizers: An Analysis and Review”, IEEE Transacitons on Evolutionary Computation, Vol.7, No. 2, pp. 117-132, 2003.
Dr. Engin Ufuk Ergul
Amasya University - Turkey