Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Local dominance and controlling dominance area of solutions in multi and many objectives EAs
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
On the complexity of computing the hypervolume indicator
IEEE Transactions on Evolutionary Computation
Conflict, harmony, and independence: relationships in evolutionary multi-criterion optimisation
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
An adaptive divide-and-conquer methodology for evolutionary multi-criterion optimisation
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Towards a quick computation of well-spread pareto-optimal solutions
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
An empirical study on the effect of mating restriction on the search ability of EMO algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Distributed computing of Pareto-optimal solutions with evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
A similarity-based mating scheme for evolutionary multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Pareto-, aggregation-, and indicator-based methods in many-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Simultaneous use of different scalarizing functions in MOEA/D
Proceedings of the 12th annual conference on Genetic and evolutionary computation
How to choose solutions for local search in multiobjective combinatorial memetic algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
IEEE Transactions on Evolutionary Computation
A dynamic Island-based genetic algorithms framework
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Hype: An algorithm for fast hypervolume-based many-objective optimization
Evolutionary Computation
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Parallelization of multi-objective evolutionary algorithms using clustering algorithms
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A review of multiobjective test problems and a scalable test problem toolkit
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
On the Evolutionary Optimization of Many Conflicting Objectives
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Local preference-inspired co-evolutionary algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Recombination of similar parents in SMS-EMOA on many-objective 0/1 knapsack problems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
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Many real-world problems have multiple competing objectives and can often be formulated as multi-objective optimisation problems. Multi-objective evolutionary algorithms (MOEAs) have proven very effective in obtaining a set of trade-off solutions for such problems. This research seeks to improve both the accuracy and the diversity of these solutions through the local application of evolutionary operators to selected sub-populations. A local operation-based implementation framework is presented in which a population is partitioned, using hierarchical clustering, into a pre-defined number of sub-populations. Environment-selection and genetic-variation are then applied to each sub-population. The effectiveness of this approach is demonstrated on 2- and 4-objective benchmark problems. The performance of each of four best-in-class MOEAs is compared with their modified local operation-based versions derived from this framework. In each case the introduction of the local operation-based approach improves performance. Further, it is shown that the combined use of local environment-selection and local genetic-variation is better than the application of either local environment-selection or local genetic-variation alone. Preliminary results indicate that the selection of a suitable number of sub-populations is related to problem dimension as well as to population size.