A new evolutionary algorithm for multiobjective optimization based on endocrine paradigm.
Rotar, Corina (2003)
Acta Universitatis Apulensis. Mathematics - Informatics
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Displaying similar documents to “Optimization using an evolutionary hyperplane guided approach.”
A new evolutionary algorithm for multiobjective optimization based on endocrine paradigm.
Bipolar convergence in genetic algorithm for multimodal optimization.
Rotar, Corina (2003)
Acta Universitatis Apulensis. Mathematics - Informatics
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A comparison of multiobjective evolutionary algorithms.
Groşan, Crina, Dumitrescu, Dumitru D. (2002)
Acta Universitatis Apulensis. Mathematics - Informatics
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An evolutionary approach to geometrical place problems.
Groşan, Crina, Oltean, Mihai, Oltean, Mihaela (2003)
Acta Universitatis Apulensis. Mathematics - Informatics
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Parameter Identification of a Fed-Batch Cultivation of S. Cerevisiae using Genetic Algorithms
Angelova, Maria, Tzonkov, Stoyan, Pencheva, Tania (2010)
Serdica Journal of Computing
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Fermentation processes as objects of modelling and high-quality control are characterized with interdependence and time-varying of process variables that lead to non-linear models with a very complex structure. This is why the conventional optimization methods cannot lead to a satisfied solution. As an alternative, genetic algorithms, like the stochastic global optimization method, can be applied to overcome these limitations. The application of genetic algorithms is a precondition for...
Global optimization by artificial life : a new technique using genetic population evolution
F. Bicking, C. Fonteix, J.-P. Corriou, I. Marc (1994)
RAIRO - Operations Research - Recherche Opérationnelle
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Mapping the convergence of genetic algorithms.
Drezner, Zvi, Marcoulides, George A. (2006)
Journal of Applied Mathematics and Decision Sciences
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Hybrid PSO and GA for global maximization.
Mesloub, Said, Mansour, Abdelouahab (2009)
International Journal of Open Problems in Computer Science and Mathematics. IJOPCM
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Theoretical analysis of steady state genetic algorithms
Alexandru Agapie, Alden H. Wright (2014)
Applications of Mathematics
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Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are probabilistic algorithms for optimization, which mimic operators from natural selection and genetics. The paper analyses the convergence of the heuristic associated to a special type of Genetic Algorithm, namely the Steady State Genetic Algorithm (SSGA), considered as a discrete-time dynamical system non-generational model. Inspired by the Markov chain results in finite Evolutionary Algorithms, conditions...
Time to the convergence of evolution in the space of population states
Iwona Karcz-Dulęba (2004)
International Journal of Applied Mathematics and Computer Science
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Phenotypic evolution of two-element populations with proportional selection and normally distributed mutation is considered. Trajectories of the expected location of the population in the space of population states are investigated. The expected location of the population generates a discrete dynamical system. The study of its fixed points, their stability and time to convergence is presented. Fixed points are located in the vicinity of optima and saddles. For large values of the standard...