This paper presents a real jumping gene genetic algorithm (RJGGA) as an enhancement of the jumping gene genetic algorithm (JGGA) [T.M. Chan, K.F. Man, K.S. Tang, S. Kwong, A jumping gene algorithm for multiobjective resource management in wideband CDMA systems, The Computer Journal 48 (6) (2005) 749-768; T.M. Chan, K.F. Man, K.S. Tang, S. Kwong, Multiobjective optimization of radio-to-fiber repeater placement using a jumping gene algorithm, in: Proceedings of the IEEE International Conference on Industrial Technology (ICIT 2005), Hong Kong, 2005, pp. 291-296; K.F. Man, T.M. Chan, K.S. Tang, S. Kwong, Jumping-genes in evolutionary computing, in: Proceedings of the IEEE IECON'2004, Busan, 2004, pp. 1268-1272]. JGGA is a relatively new multiobjective evolutionary algorithm (MOEA) that imitates a jumping gene phenomenon discovered by Nobel Laureate McClintock during her work on the corn plants. The main feature of JGGA is that it only has a simple operation in which a transposition of gene(s) is induced within the same or another chromosome in the genetic algorithm (GA) framework. In its initial formulation, the search space solutions are binary-coded and it inherits the customary problems of conventional binary-coded GA (BCGA). This issue motivated us to remodel the JGGA into RJGGA. The performance of RJGGA has been compared to other MOEAs using some carefully chosen benchmark test functions. It has been observed that RJGGA is able to generate non-dominated solutions with a wider spread along the Pareto-optimal front and better address the issues regarding convergence and diversity in multiobjective optimization. © 2006 Elsevier Inc. All rights reserved.
Bibliographical noteThe authors wish to thank Prof. Brian Ralph of Brunel University for his feedback. His comments helped us to substantially improve the quality of this paper and to make it more readable. We also gratefully acknowledge the support from City University Strategic Grant 7001955.
- Benchmark test functions
- Jumping gene genetic algorithm
- Jumping genes
- Multiobjective evolutionary algorithms
- Performance metrics