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Hernández-Díaz, A.G., Santana-Quintero, L.V., Coello Coello, C.A., Caballero, R. and Molina, J. (2008) Improving Multi-Objective Evolutionary Algorithms by Using Rough Sets. In: Ligeza, A., Reich, S., Schaefer, R. and Cotta, C., Eds., Knowledge-Driven Computing: Knowledge Engineering and Intelligent Computations, Studies in Computational Intelligence, Vol. 102, Springer-Verlag, Berlin, 81-98.

has been cited by the following article:

• TITLE: Local Search-Inspired Rough Sets for Improving Multiobjective Evolutionary Algorithm

  AUTHORS: Ahmed A. EL-Sawy, Mohamed A. Hussein, El-Sayed Mohamed Zaki, Abd Allah A. Mousa

  KEYWORDS: Multiobjective Optimization, Genetic Algorithms, Rough Sets Theory

  JOURNAL NAME: Applied Mathematics, Vol.5 No.13, July 15, 2014

  ABSTRACT: In this paper we present a new optimization algorithm, and the proposed algorithm operates in two phases. In the first one, multiobjective version of genetic algorithm is used as search engine in order to generate approximate true Pareto front. This algorithm is based on concept of co-evolution and repair algorithm for handling nonlinear constraints. Also it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept e-dominance. Then, in the second stage, rough set theory is adopted as local search engine in order to improve the spread of the solutions found so far. The results, provided by the proposed algorithm for benchmark problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our algorithm is better applicable for solving real-world application problems.