Search, polynomial complexity, and the fast messy genetic algorithm
Search, polynomial complexity, and the fast messy genetic algorithm
Multiobjective optimization with messy genetic algorithms
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
The combative accretion model – multiobjective optimisation without explicit pareto ranking
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Computers and Industrial Engineering
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Many real-world scientific and engineering applications involve finding innovative solutions to \hard" Multiobjective Optimization Problems (MOP). Various Multiobjective Evolutionary Algorithms (MOEA) have been developed to obtain MOP Pareto solutions. A particular exciting MOEA is the MOMGA which is an extension of the single-objective building block (BB) based messy Genetic Algorithm. The intent of this discussion is to illustrate that modifications made to the Multi-Objective messy GA (MOMGA) have further improved its efficiency resulting in the MOMGA-II. The MOMGA-II uses a probabilistic BB approach to initializing the population referred to as Probabilistically Complete Initialization. This has the effect of improving the efficiency of the MOMGA through the reduction of computational bottle-necks. Similar statistical results have been obtained using the MOMGA-II as compared to the results of the original MOMGA as well as those obtained by other MOEAs as tested with standard generic MOP test suites.