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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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Optimum design of high-rise steel buildings using an evolution strategy integrated parallel algorithm
TL;DR: The present study addresses a parallel solution algorithm for optimum design of large steel space frame structures, in particular high-rise steel buildings, with a novel discrete evolution strategy optimization method to effectively size these systems for minimum weight according to the provisions of ASD-AISC specification and various practical aspects of design process.
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Two-Loop Real-Coded Genetic Algorithms with Adaptive Control of Mutation Step Sizes
Francisco Herrera,Manuel Lozano +1 more
TL;DR: This paper presents TRAMSS, a Two-loop Real-coded genetic algorithm with Adaptive control of Mutation Step Sizes, offering two main advantages simultaneously, better reliability and accuracy.
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Acceleration of the convergence speed of evolutionary algorithms using multi-layer neural networks
TL;DR: The proposed acceleration method implements an online multi-layer neural network approximating the fitness calculation, which greatly decreases the computation time because the time-consuming fitness calculation can be replaced by the simple network output.
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A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems
TL;DR: A relevance feedback process for extended Boolean (fuzzy) information retrieval systems based on a hybrid evolutionary algorithm combining simulated annealing and genetic programming components is introduced.
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Performance analysis of evolutionary optimization with cumulative step length adaptation
Dirk V. Arnold,Hans-Georg Beyer +1 more
TL;DR: This paper presents a theoretical analysis of the cumulative step length adaptation approach to evolution strategies, and includes the practically relevant case of noise interfering in the optimization process.