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
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Computing on Consumer Graphics Hardware
IEEE Intelligent Systems
A data parallel approach to genetic programming using programmable graphics hardware
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Towards a quick computation of well-spread pareto-optimal solutions
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Fast genetic programming on GPUs
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
A SIMD interpreter for genetic programming on GPU graphics cards
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Properties of an adaptive archiving algorithm for storing nondominated vectors
IEEE Transactions on Evolutionary Computation
Dynamic multiobjective evolutionary algorithm: adaptive cell-based rank and density estimation
IEEE Transactions on Evolutionary Computation
Using unconstrained elite archives for multiobjective optimization
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Evaluating the cell broadband engine as a platform to run estimation of distribution algorithms
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Deployment of CPU and GPU-based genetic programming on heterogeneous devices
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
ACO with tabu search on a GPU for solving QAPs using move-cost adjusted thread assignment
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Highly scalable multi objective test suite minimisation using graphics cards
SSBSE'11 Proceedings of the Third international conference on Search based software engineering
Journal of Parallel and Distributed Computing
The asynchronous island model and NSGA-II: study of a new migration operator and its performance
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Most real-life optimization problems or decision-making problems are multi-objective in nature, since they normally have several (possibly conflicting) objectives that must be satisfied at the same time. Multi-Objective Evolutionary Algorithms (MOEAs) have been gaining increasing attention among researchers and practitioners. However, they may execute for a long time for some difficult problems, because several evaluations must be performed. Moreover, the non-dominance checking and the non-dominated selection procedures are also very time consuming. From our experiments, more than 99% of the execution time is used in performing the two procedures. A promising approach to overcome this limitation is to parallelize these algorithms. In this paper, we propose a parallel MOEA on consumer-level Graphics Processing Units (GPU). We perform many experiments on two-objective and three-objective benchmark problems to compare our parallel MOEA with a sequential MOEA and demonstrate that the former is much more efficient than the latter.