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Evolutionary algorithms in theory and practice

Thomas Bäck
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.

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A self-adjusting e-course generation process for personalized learning

TL;DR: The investigation results indicate that the proposed self-adjusting e-course generation process adapts to learners by utilizing the feedback from many learners, which support to provide a truly personalized learning environment.
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Forecasting call frequency at a financial services call centre

TL;DR: A model with a dynamic level, multiplicative calendar effects and a multiplicative advertising response is developed and shown to be effective for the number of daily applications for loans at a financial services telephone call centre.
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Genetic algorithms optimization for normalized normal constraint method under Pareto construction

TL;DR: The results presented in this paper show a suitable behavior for the genetic algorithms method compared to classical Gauss-Newton optimization methods which are used by the original normalized normal constraint method.
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EVO—Evolutionary algorithm for crystal structure prediction

TL;DR: EVO successfully demonstrates its applicability to find crystal structures of the elements of the 3rd main group with their different spacegroups by using the number of atoms in the conventional cell and multiples of it.
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State‐dependent energy allocation in variable environments: life history evolution of a rotifer

TL;DR: The model's predictions about body size, age at maturity, and effects of food deprivation are consistent with experimental results for B. calyciflorous, indicating that these life history traits have all been shaped by evolution of energy allocation and storage strategies in response to environmental variability.