Curves and surfaces for computer aided geometric design
Curves and surfaces for computer aided geometric design
A collection of test problems for constrained global optimization algorithms
A collection of test problems for constrained global optimization algorithms
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Concrete Math
An Empirical Comparison of Selection Methods in Evolutionary Algorithms
Selected Papers from AISB Workshop on Evolutionary Computing
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Improving global numerical optimization using a search-space reduction algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Generalizing surrogate-assisted evolutionary computation
IEEE Transactions on Evolutionary Computation
Simulation optimization based on Taylor Kriging and evolutionary algorithm
Applied Soft Computing
Block-matching algorithm based on differential evolution for motion estimation
Engineering Applications of Artificial Intelligence
Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)
Applied Soft Computing
A novel evolutionary algorithm inspired by the states of matter for template matching
Expert Systems with Applications: An International Journal
Block-matching algorithm based on harmony search optimization for motion estimation
Applied Intelligence
| Hi-index | 0.00 |
The problem of finding optimal values in complex parameter optimization problems has often been solved with success by evolutionary algorithms (EAs). In many cases, these algorithms are employed as black-box methods over imprecisely known domains. Such problems arise frequently in engineering design. The principal barrier to the general use of EAs for those problems is the huge number of function evaluations that is often required. This makes EAs an impractical approach when the function evaluation depends on numerically heavy design analysis tools, for example, finite elements methods. This paper presents the use of kriging interpolation as a function approximation method for the construction of an internal model of the fitness landscape. This model is intended to guide the search process with a reduced number of fitness function evaluations.