Abstract—In evolutionary computation, several multi-objective genetic algorithms (MOGAs) have been widely used to solve multi-objective optimization problems (MOOPs). The version NSGA-II, developed by Deb et al., is a useful package using a population-based genetic algorithm to solve optimization problems with multiple objectives subject to constraints. This study proposes an enhanced version of NSGA-II, termed LS-EMOGA herein, which modifies the crossover and mutation operators of original NSGA-II by an extended intermediate crossover and a non-uniform mutation and also incorporates a local search (LS) procedure to improve the fine-turning ability of the solution searching. The performance of the proposed LS-EMOGA is assessed by evaluating five benchmark cases of MOOPs. The computed solutions are compared with those of obtained using NSGA-II and proposed MOGA without local search procedure (EMOGA version). Moreover, the proposed LS-EMOGA combines a k-means clustering algorithm to apply to the case diagnosis of gestational diabetic disease.
Index Terms—enhanced multi-objective genetic algorithms, local search, k-means clustering algorithm, gestational diabetic disease
Cite: Jenn-Long Liu, Chung-Chih Li, and Chien-Liang Chen, "Local Search-based Enhanced Multi-objective Genetic Algorithm and Its Application to the Gestational Diabetes Diagnosis," Vol. 6, No. 4, pp. 252-257, November, 2015. doi: 10.12720/jait.6.4.252-257
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