Please use this identifier to cite or link to this item: https://doi.org/10.1002/9781118341704.ch4
Title: Performance Comparison of Jumping Gene Adaptations of the Elitist Non-dominated Sorting Genetic Algorithm
Authors: Sharma, S.
Nabavi, S.R.
Rangaiah, G.P. 
Keywords: Elitist nondominated sorting genetic algorithm
Jumping-gene adaptations
Multi-objective optimization
Issue Date: 2-Apr-2013
Source: Sharma, S.,Nabavi, S.R.,Rangaiah, G.P. (2013-04-02). Performance Comparison of Jumping Gene Adaptations of the Elitist Non-dominated Sorting Genetic Algorithm. Multi-Objective Optimization in Chemical Engineering: Developments and Applications : 103-127. ScholarBank@NUS Repository. https://doi.org/10.1002/9781118341704.ch4
Abstract: Industrial problems are complex in nature, and often have multiple performance criteria. The elitist nondominated sorting genetic algorithm (NSGA-II) has been used to optimize many process design and operation problems for two or more objectives. In order to improve the performance of this algorithm, the jumping-gene concept from natural genetics has been incorporated in NSGA-II. Several jumping-gene adaptations have been proposed and used to solve mathematical and application problems in different studies. In this chapter, four jumping-gene adaptations are selected and comprehensively evaluated on a number of two-objective unconstrained and constrained test functions. Three quality metrics, namely, generational distance, spread and inverse generational distance are employed to evaluate the distribution and convergence of the obtained Pareto-optimal solutions at the final generation and also at selected intermediate generations. Additionally, a search termination criterion based on the improvement in the Pareto-optimal front, has been described and used to check convergence of NGSA-II with the selected jumping gene adaptations. © 2013 John Wiley & Sons, Ltd.
Source Title: Multi-Objective Optimization in Chemical Engineering: Developments and Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/68013
ISBN: 9781118341667
DOI: 10.1002/9781118341704.ch4
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