1 research outputs found
Improved NSGA-II Based on a Novel Ranking Scheme
Non-dominated Sorting Genetic Algorithm (NSGA) has established itself as a
benchmark algorithm for Multiobjective Optimization. The determination of
pareto-optimal solutions is the key to its success. However the basic algorithm
suffers from a high order of complexity, which renders it less useful for
practical applications. Among the variants of NSGA, several attempts have been
made to reduce the complexity. Though successful in reducing the runtime
complexity, there is scope for further improvements, especially considering
that the populations involved are frequently of large size. We propose a
variant which reduces the run-time complexity using the simple principle of
space-time trade-off. The improved algorithm is applied to the problem of
classifying types of leukemia based on microarray data. Results of comparative
tests are presented showing that the improved algorithm performs well on large
populations