Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Journal of Global Optimization
Training products of experts by minimizing contrastive divergence
Neural Computation
Bayesian Optimization Algorithms for Multi-objective Optimization
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Evolutionary Multi-objective Ranking with Uncertainty and Noise
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Multiobjective Evolutionary Algorithms and Applications (Advanced Information and Knowledge Processing)
Multiobjective hBOA, clustering, and scalability
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Multi-objective test problems, linkages, and evolutionary methodologies
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
Adaptive variance scaling in continuous multi-objective estimation-of-distribution algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A Decision-Tree-Based Multi-objective Estimation of Distribution Algorithm
CIS '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security
Training restricted Boltzmann machines using approximations to the likelihood gradient
Proceedings of the 25th international conference on Machine learning
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
MOPED: a multi-objective parzen-based estimation of distribution algorithm for continuous problems
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Multiobjective Estimation of Distribution Algorithm Combined with PSO for RFID Network Optimization
ICMTMA '10 Proceedings of the 2010 International Conference on Measuring Technology and Mechatronics Automation - Volume 02
Niching without niching parameters: particle swarm optimization using a ring topology
IEEE Transactions on Evolutionary Computation
Research frontier: linkage discovery through data mining
IEEE Computational Intelligence Magazine
Memetic compact differential evolution for cartesian robot control
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
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Rank-density-based multiobjective genetic algorithm and benchmark test function study
IEEE Transactions on Evolutionary Computation
A review of multiobjective test problems and a scalable test problem toolkit
IEEE Transactions on Evolutionary Computation
An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm
IEEE Transactions on Evolutionary Computation
Multi-objective optimization with estimation of distribution algorithm in a noisy environment
Evolutionary Computation
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Multi-objective optimization is widely found in many fields, such as logistics, economics, engineering, or whenever optimal decisions need to be made in the presence of tradeoff between two or more conflicting objectives. The synergy of probabilistic graphical approaches in evolutionary mechanism may enhance the iterative search process when interrelationships of the archived data has been learned, modeled, and used in the reproduction. This paper presents the implementation of probabilistic graphical approaches in solving multi-objective optimization problems under the evolutionary paradigm. First, the existing work on the synergy between probabilistic graphical models and evolutionary algorithms in the multi-objective framework will be presented. We will then show that the optimization problems can be solved using a restricted Boltzmann machine (RBM). The learning, modeling as well as sampling mechanisms of the RBM will be highlighted. Lastly, five studies that implement the RBM for solving multi-objective optimization problems will be discussed.