A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization
Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for
solving expensive optimization problems where only a small number of real fitness …
solving expensive optimization problems where only a small number of real fitness …
Ensemble of differential evolution variants
Differential evolution (DE) is one of the most popular and efficient evolutionary algorithms for
numerical optimization and it has gained much success in a series of academic benchmark …
numerical optimization and it has gained much success in a series of academic benchmark …
Differential evolution with multi-population based ensemble of mutation strategies
Differential evolution (DE) is among the most efficient evolutionary algorithms (EAs) for
global optimization and now widely applied to solve diverse real-world applications. As the …
global optimization and now widely applied to solve diverse real-world applications. As the …
Surrogate-assisted cooperative swarm optimization of high-dimensional expensive problems
Surrogate models have shown to be effective in assisting metaheuristic algorithms for
solving computationally expensive complex optimization problems. The effectiveness of …
solving computationally expensive complex optimization problems. The effectiveness of …
Ensemble strategies for population-based optimization algorithms–A survey
In population-based optimization algorithms (POAs), given an optimization problem, the
quality of the solutions depends heavily on the selection of algorithms, strategies and …
quality of the solutions depends heavily on the selection of algorithms, strategies and …
Improving metaheuristic algorithms with information feedback models
In most metaheuristic algorithms, the updating process fails to make use of information
available from individuals in previous iterations. If this useful information could be exploited …
available from individuals in previous iterations. If this useful information could be exploited …
Generalized multitasking for evolutionary optimization of expensive problems
Conventional evolutionary algorithms (EAs) are not well suited for solving expensive
optimization problems due to the fact that they often require a large number of fitness …
optimization problems due to the fact that they often require a large number of fitness …
Data-driven surrogate-assisted multiobjective evolutionary optimization of a trauma system
Most existing work on evolutionary optimization assumes that there are analytic functions for
evaluating the objectives and constraints. In the real world, however, the objective or …
evaluating the objectives and constraints. In the real world, however, the objective or …
A surrogate-assisted differential evolution algorithm for high-dimensional expensive optimization problems
The radial basis function (RBF) model and the Kriging model have been widely used in the
surrogate-assisted evolutionary algorithms (SAEAs). Based on their characteristics, a global …
surrogate-assisted evolutionary algorithms (SAEAs). Based on their characteristics, a global …
Efficient generalized surrogate-assisted evolutionary algorithm for high-dimensional expensive problems
Engineering optimization problems usually involve computationally expensive simulations
and many design variables. Solving such problems in an efficient manner is still a major …
and many design variables. Solving such problems in an efficient manner is still a major …