The Jumping Gene Operator

It is noteworthy that the JG operator has also been successfully incorporated into a Multi-objective Simulated Annealing technique (Sankararao and Gupta, 2007 see Chapter 4 in this book). The performance assessment of this algorithm was done on three well-known test (benchmark) problems commonly used in the evolutionary MOO field. This algorithm was then employed for the MOO of an industrial fluidized-bed catalytic cracking unit. 

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The use of the jumping gene operator (any of the adaptations) increases the randomness/diversity and, thus, usually gives better results. Among the various JG adaptations discussed above, the sJG adaptation is found to be better for the benchmark problems studied here. But the choice of a particular adaptation is problem-specific, e.g., in froth flotation circuits the mJG adaptation (Guria et al., 2005b) is found to be better. 

Kasat, R. B. and Gupta, S. K. (2003). Multiobjective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using genetic algorithm with the jumping gene operator, Comput. Chem. Eng., 27, pp. 1785-1800. 

Froth floatation circuits for mineral processing Maximization of both the recovery of the concentrated ore and valuable mineral content in the concentrated ore. NSGA-II with modified Jumping Gene operator Equality constraint was imposed on total floatation cell volume. Guria et al. (2005a)... 

Sankararao, B. and Gupta, S. K. (2006). Multi-ohjective optimization of the d3mamic operation of an industrial steam reformer using the jumping gene adaptations of simulated asmealmg, Asia-Pac. J. Ghent. Eng., 1, pp. 21-31. 

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