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Evolutionary algorithms in theory and practice
TLDR
In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming within a unified framework, thereby clarifying the similarities and differences of these methods.About:
The article was published on 1996-01-01 and is currently open access. It has received 2679 citations till now. The article focuses on the topics: Evolutionary music & Evolutionary programming.read more
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Reactive distillation for multiple-reaction systems: optimisation study using an evolutionary algorithm
TL;DR: In this article, an evolutionary algorithm was applied and the obtained results were compared with those of a conventional stirred tank reactor to quantify the potential of RD to improve the target product selectivity of multiple-reaction systems.
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An adaptive and opposite K-means operation based memetic algorithm for data clustering
TL;DR: In this article, a hybrid EA based clustering framework is introduced, where the frequency and intensity of k-means operator can be arbitrarily configured during the evolution of the algorithm.
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Speckle Reduction Through Interactive Evolution of a General Order Statistics Filter for Clinical Ultrasound Imaging
TL;DR: An interactive tool performing adaptive speckle filtering so that the medical expert who runs the algorithm has permanent control over the output and guides the process towards obtaining enhanced images that agree to his/her subjective quality criteria is presented.
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Determination of relative agrarian technical efficiency by a dynamic over-sampling procedure guided by minimum sensitivity
Francisco Fernández-Navarro,César Hervás-Martínez,Carlos R. García-Alonso,Mercedes Torres-Jiménez +3 more
TL;DR: A dynamic over-sampling procedure is proposed to improve the classification of imbalanced datasets with more than two classes and is able to improve minimum sensitivity in the generalization set and obtains a high accuracy level.
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Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification
TL;DR: This framework automates both the feature learning and model building processes and constructs a suitable model to understand the characteristic features for different tasks, demonstrating that a time-consuming task could also be conducted by an automated process that exceeds the human ability.