The nature of statistical learning theory
The nature of statistical learning theory
Extending Population-Based Incremental Learning to Continuous Search Spaces
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Expanding from Discrete to Continuous Estimation of Distribution Algorithms: The IDEA
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
On the importance of diversity maintenance in estimation of distribution algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The correlation-triggered adaptive variance scaling IDEA
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)
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)
SDR: a better trigger for adaptive variance scaling in normal EDAs
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Cross entropy and adaptive variance scaling in continuous EDA
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
Enhancing the Performance of Maximum---Likelihood Gaussian EDAs Using Anticipated Mean Shift
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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We describe a parameter-free estimation-of-distribution algorithm EDA called the adapted maximum-likelihood Gaussian model iterated density-estimation evolutionary algorithm AMaLGaM-IDA, or AMaLGaM for short for numerical optimization. AMaLGaM is benchmarked within the 2009 black box optimization benchmarking BBOB framework and compared to a variant with incremental model building iAMaLGaM. We study the implications of factorizing the covariance matrix in the Gaussian distribution, to use only a few or no covariances. Further, AMaLGaM and iAMaLGaM are also evaluated on the noisy BBOB problems and we assess how well multiple evaluations per solution can average out noise. Experimental evidence suggests that parameter-free AMaLGaM can solve a wide range of problems efficiently with perceived polynomial scalability, including multimodal problems, obtaining the best or near-best results among all algorithms tested in 2009 on functions such as the step-ellipsoid and Katsuuras, but failing to locate the optimum within the time limit on skew Rastrigin-Bueche separable and Lunacek bi-Rastrigin in higher dimensions. AMaLGaM is found to be more robust to noise than iAMaLGaM due to the larger required population size. Using few or no covariances hinders the EDA from dealing with rotations of the search space. Finally, the use of noise averaging is found to be less efficient than the direct application of the EDA unless the noise is uniformly distributed. AMaLGaM was among the best performing algorithms submitted to the BBOB workshop in 2009.