Journal of Computer Science and Technology, 2005 This work is focussed on the development and analysis of a new class of algorithms, called cellular memetic algorithms (cMAs), which will be evaluated here on the satisfiability problem (SAT). For describing a cMA, we study the effects of adding specific knowledge of the problem to the fitness function, the crossover and mutation operators, and to the ...
Alba Torres, Enrique +2 more
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An Accelerated Introduction to Memetic Algorithms
, 2018 Política de acceso abierto tomada de: https://www.springernature.com/gp/open-science/policies/book ...
Moscato, Pablo, Cotta-Porras, Carlos
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Sensor Placement in an Irregular 3D Surface for Improving Localization Accuracy Using a Multi-Objective Memetic Algorithm. [PDF]
Sensors (Basel), 2023 Graça PA, Alves JC, Ferreira BM.
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Automated Generation of Cross-Domain Analogies via Evolutionary Computation
, 2012 Analogy plays an important role in creativity, and is extensively used in science as well as art. In this paper we introduce a technique for the automated generation of cross-domain analogies based on a novel evolutionary algorithm (EA).
Baydin, Atilim Gunes +2 more
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A Time-Dependent Vehicle Routing Problem for Instant Delivery Based on Memetic Algorithm. [PDF]
Comput Intell Neurosci, 2022 Cui S, Sun Q, Zhang Q.
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An Improved Memetic Algorithm for Detecting Protein Complexes in Protein Interaction Networks. [PDF]
Front Genet, 2021 Wang R, Ma H, Wang C.
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A Modified Memetic Algorithm with an Application to Gene Selection in a Sheep Body Weight Study. [PDF]
Animals (Basel), 2022 Miao M, Wu J, Cai F, Wang YG.
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A Novel Memetic Algorithm Based on Multiparent Evolution and Adaptive Local Search for Large-Scale Global Optimization. [PDF]
Comput Intell Neurosci, 2022 Zhang W, Lan Y.
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A Memetic Algorithm for Solving the Robust Influence Maximization Problem on Complex Networks against Structural Failures. [PDF]
Sensors (Basel), 2022 Huang D, Tan X, Chen N, Fan Z.
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International Journal of Computational Intelligence SystemsDifferential evolution (DE) represents a class of population-based optimization techniques that uses differences of vectors to search for optimal solutions in the search space.
Miguel Leon +3 more
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