Search results for "Evolutionary algorithms"

showing 4 items of 24 documents

NASH EVOLUTIONARY ALGORITHMS: TESTING PROBLEM SIZE IN RECONSTRUCTION PROBLEMS IN FRAME STRUCTURES

2016

The use of evolutionary algorithms has been enhanced in recent years for solving real engineering problems, where the requirements of intense computational calculations are needed, especially when computational engineering simulations are involved (use of finite element method, boundary element method, etc). The coupling of game-theory concepts in evolutionary algorithms has been a recent line of research which could enhance the efficiency of the optimum design procedure and the quality of the design solutions achieved. They have been applied in several fields of engineering and sciences, mainly, in aeronautical and structural engineering (e.g: in computational fluid dynamics and solid mech…

frame optimizationFrame (networking)Evolutionary algorithmgame strategiesstructural optimizationrakennesuunnitteluevolutionary algorithmsAlgorithmNash equilibriumMathematicsProceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)
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Parallel global optimization : structuring populations in differential evolution

2010

metaheuristicsoptimointistagnaatioglobal optimizationalgoritmitdifferentiaali evoluutioevoluutiolaskentaDifferential EvolutionEvolutionary computationevolutionary algorithmsmatemaattinen optimointiglobaali optimointitietojenkäsittely
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Simple memetic computing structures for global optimization

2014

optimointidifferentiaalievoluutiomemetic computingdifferential evolutionlocal searchmemeettiset algoritmitgeneettiset algoritmitmemetic algorithmsevolutionary algorithmsmemetic structures
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Synchronous R-NSGA-II: An Extended Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

2015

Classical evolutionary multi-objective optimization algorithms aim at finding an approx- imation of the entire set of Pareto optimal solutions. By considering the preferences of a decision maker within evolutionary multi-objective optimization algorithms, it is possible to focus the search only on those parts of the Pareto front that satisfy his/her preferences. In this paper, an extended preference-based evolutionary algorithm has been proposed for solving multi-objective optimiza- tion problems. Here, concepts from an interactive synchronous NIMBUS method are borrowed and combined with the R-NSGA-II algorithm. The proposed synchronous R-NSGA-II algorithm uses preference information provid…

ta113Mathematical optimizationinteractive multi-objective optimizationApplied MathematicsEvolutionary algorithmApproxDecision makerMulti-objective optimizationscalarizing functionSet (abstract data type)Pareto optimalevolutionary multi-objective optimizationpreference-based evolutionary algorithmsFocus (optics)Preference (economics)Information SystemsMathematicsInformatica
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