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
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Journal of Global Optimization
Training products of experts by minimizing contrastive divergence
Neural Computation
Papers from an international workshop on Towards Evolvable Hardware, The Evolutionary Engineering Approach
Bayesian Optimization Algorithms for Multi-objective Optimization
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Pareto-Front Exploration with Uncertain Objectives
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
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
Fitness inheritance for noisy evolutionary multi-objective optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
An overview of evolutionary algorithms in multiobjective optimization
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
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Localization for solving noisy multi-objective optimization problems
Evolutionary Computation
Towards the validation of plagiarism detection tools by means of grammar evolution
IEEE Transactions on Evolutionary Computation
Evolutionary multiobjective optimization in noisy problem environments
Journal of Heuristics
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
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
Protein interaction inference using particle swarm optimization algorithm
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Research frontier: linkage discovery through data mining
IEEE Computational Intelligence Magazine
Computational evolutionary embryogeny
IEEE Transactions on Evolutionary Computation
Research frontier: memetic computation-past, present & future
IEEE Computational Intelligence Magazine
Towards a memetic feature selection paradigm
IEEE Computational Intelligence Magazine
Memetic compact differential evolution for cartesian robot control
IEEE Computational Intelligence Magazine
A preliminary study on handling uncertainty in indicator-based multiobjective optimization
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Multi-objective optimization of problems with epistemic uncertainty
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithm for the optimization of noisy combustion processes
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm
IEEE Transactions on Evolutionary Computation
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
| Hi-index | 0.00 |
Many real-world optimization problems are subjected to uncertainties that may be characterized by the presence of noise in the objective functions. The estimation of distribution algorithm EDA, which models the global distribution of the population for searching tasks, is one of the evolutionary computation techniques that deals with noisy information. This paper studies the potential of EDAs; particularly an EDA based on restricted Boltzmann machines that handles multi-objective optimization problems in a noisy environment. Noise is introduced to the objective functions in the form of a Gaussian distribution. In order to reduce the detrimental effect of noise, a likelihood correction feature is proposed to tune the marginal probability distribution of each decision variable. The EDA is subsequently hybridized with a particle swarm optimization algorithm in a discrete domain to improve its search ability. The effectiveness of the proposed algorithm is examined via eight benchmark instances with different characteristics and shapes of the Pareto optimal front. The scalability, hybridization, and computational time are rigorously studied. Comparative studies show that the proposed approach outperforms other state of the art algorithms.