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- Test_functions_for_optimization abstract "In applied mathematics, test functions, known as artificial landscapes, are useful to evaluate characteristics of optimization algorithms, such as: Velocity of convergence. Precision. Robustness. General performance.Here some test functions are presented with the aim of giving an idea about the different situations that optimization algorithms have to face when coping with these kinds of problems. In the first part, some objective functions for single-objective optimization cases are presented. In the second part, test functions with their respective Pareto fronts for multi-objective optimization problems (MOP) are given.The artificial landscapes presented herein for single-objective optimization problems are taken from Bäck, Haupt et. al. and from Rody Oldenhuis software. Given the amount of problems (55 in total), just a few are presented here. The complete list of test functions is found on the Mathworks website.The test functions used to evaluate the algorithms for MOP were taken from Deb, Binh et. al. and Binh. You can download the software developed by Deb, which implements the NSGA-II procedure with GAs, or the program posted on Internet, which implements the NSGA-II procedure with ES.Just a general form of the equation, a plot of the objective function, boundaries of the object variables and the coordinates of global minima are given herein.".
- Test_functions_for_optimization wikiPageID "37526558".
- Test_functions_for_optimization wikiPageRevisionID "605161527".
- Test_functions_for_optimization hasPhotoCollection Test_functions_for_optimization.
- Test_functions_for_optimization subject Category:Constraint_programming.
- Test_functions_for_optimization subject Category:Convex_optimization.
- Test_functions_for_optimization subject Category:Mathematical_optimization.
- Test_functions_for_optimization subject Category:Types_of_functions.
- Test_functions_for_optimization comment "In applied mathematics, test functions, known as artificial landscapes, are useful to evaluate characteristics of optimization algorithms, such as: Velocity of convergence. Precision. Robustness. General performance.Here some test functions are presented with the aim of giving an idea about the different situations that optimization algorithms have to face when coping with these kinds of problems. In the first part, some objective functions for single-objective optimization cases are presented.".
- Test_functions_for_optimization label "Test functions for optimization".
- Test_functions_for_optimization sameAs Funzione_test_(ottimizzazione).
- Test_functions_for_optimization sameAs m.0nbtsfl.
- Test_functions_for_optimization sameAs Q7705773.
- Test_functions_for_optimization sameAs Q7705773.
- Test_functions_for_optimization wasDerivedFrom Test_functions_for_optimization?oldid=605161527.
- Test_functions_for_optimization isPrimaryTopicOf Test_functions_for_optimization.