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The correlation-triggered adaptive variance scaling IDEA
TLDR
In this paper, an adaptive variance scaling theme is introduced that aims at reducing the risk of premature convergence in continuous Estimation of Distribution Algorithms. Expand
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Enhancing the Performance of Maximum-Likelihood Gaussian EDAs Using Anticipated Mean Shift
TLDR
We focus on a second source of inefficiency that is not removed by existing remedies. Expand
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Matching inductive search bias and problem structure in continuous Estimation-of-Distribution Algorithms
TLDR
We show that without careful interpretation and adaptation of lessons learned from discrete EDAs, continuous EDAs will fail to perform efficient optimization on even some of the simplest problems. Expand
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Developing Genetic Algorithms and Mixed Integer Linear Programs for Finding Optimal Strategies for a Student’s “Sports” Activity
An important advantage of genetic algorithms (GAs) are their ease of use, their wide applicability, and their good performance for a wide range of different problems. GAs are able to find goodExpand
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Scalability of using Restricted Boltzmann Machines for combinatorial optimization
TLDR
We integrate an RBM into an EDA and evaluate the performance of this system in solving combinatorial optimization problems with a single objective. Expand
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SDR: a better trigger for adaptive variance scaling in normal EDAs
TLDR
We introduce the Standard-DeviationRatio (SDR) trigger that is integrated with theIterated Density-Estimation Evolutionary Algorithm(IDEA) for continuous, normal-distribution-based Estimation-of-DistributionAlgorithms by scaling the variance up from the maximum-likelihood estimate. Expand
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Benchmarking Parameter-Free AMaLGaM on Functions With and Without Noise
TLDR
We describe a parameter-free estimation-of-distribution algorithm (EDA) called the adapted maximum-likelihood Gaussian model iterated density-estimation evolutionary algorithm (AMaLGaM for short) for numerical optimization. Expand
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Collaboration Networks in the Music Industry
TLDR
We studied the large-scale structure of music collaborations using the tools of network science. Expand
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Meta-heuristics for placing strategic safety stock in multi-echelon inventory with differentiated service times
TLDR
We propose a representation for the safety stock allocation problem with differentiated service times that can be used in general-acyclic supply networks. Expand
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A Problem-Adjusted Genetic Algorithm for Flexibility Design
TLDR
We develop a Flexibility Design Genetic Algorithm (FGA) that exploits qualitative insights into the structure of good solutions, such as the well-established chaining principle, to enhance its performance. Expand
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