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Non-dominated Sorting Genetic Algorithm (NSGA) has established itself as abenchmark algorithm for Multiobjective Optimization. The determination ofpareto-optimal solutions is the key to its success. However the basic algorithmsuffers from a high order of complexity, which renders it less useful forpractical applications. Among the variants of NSGA, several attempts have beenmade to reduce the complexity. Though successful in reducing the runtimecomplexity, there is scope for further improvements, especially consideringthat the populations involved are frequently of large size. We propose avariant which reduces the run-time complexity using the simple principle ofspace-time trade-off. The improved algorithm is applied to the problem ofclassifying types of leukemia based on microarray data. Results of comparativetests are presented showing that the improved algorithm performs well on largepopulations.

Improved NSGA-II Based on a Novel Ranking Scheme