Abstract
This paper introduces a new algorithm, termed as the ε-NSGA-II that enables the user to specify the precision with which they want to quantify the Pareto optimal set and all other parameters are automatically specified within the algorithm. The development of the ε-NSGA-II was motivated by the steady state ε-MOEA developed by Deb et al. [3]. The next section briefly describes the ε-NSGA-II.
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References
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons LTD, New York (2001)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8(2), 125–148 (2000)
Deb, K., Mohan, M., Mishra, S.: A Fast Multi-objective Evolutionary Algorithm for Finding Well-Spread Pareto-Optimal Solutions. Indian Institute of Technology, Kanpur (2003)
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© 2004 Springer-Verlag Berlin Heidelberg
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Devireddy, V., Reed, P. (2004). Efficient and Reliable Evolutionary Multiobjective Optimization Using ε-Dominance Archiving and Adaptive Population Sizing. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_38
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DOI: https://doi.org/10.1007/978-3-540-24855-2_38
Publisher Name: Springer, Berlin, Heidelberg
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