Evolutionary optimization methods for high-dimensional expensive problems: A survey
Evolutionary computation is a rapidly evolving field and the related algorithms have been
successfully used to solve various real-world optimization problems. The past decade has …
successfully used to solve various real-world optimization problems. The past decade has …
An autoencoder-embedded evolutionary optimization framework for high-dimensional problems
Many ever-increasingly complex engineering optimization problems fall into the class of
High-dimensional Expensive Problems (HEPs), where fitness evaluations are very time …
High-dimensional Expensive Problems (HEPs), where fitness evaluations are very time …
Surrogate-assisted autoencoder-embedded evolutionary optimization algorithm to solve high-dimensional expensive problems
Surrogate-assisted evolutionary algorithms (EAs) have been intensively used to solve
computationally expensive problems with some success. However, traditional EAs are not …
computationally expensive problems with some success. However, traditional EAs are not …
Incorporating gradient information into dimension perturbation mutation for high-dimensional expensive optimization
Surrogate-assisted evolutionary algorithms (SAEAs) have recently shown excellent abilities
in solving computationally expensive optimization problems (EOPs), but most of them are …
in solving computationally expensive optimization problems (EOPs), but most of them are …
Classification model-based and assisted environment selection for evolutionary algorithms to solve high-dimensional expensive problems
Surrogate-assisted evolutionary algorithms (SAEAs) have been proven to be very effective
in tackling low-dimensional expensive problems. However, it remains a challenge to solve …
in tackling low-dimensional expensive problems. However, it remains a challenge to solve …
Turning high-dimensional optimization into computationally expensive optimization
Divide-and-conquer (DC) is conceptually well suited to deal with high-dimensional
optimization problems by decomposing the original problem into multiple low-dimensional …
optimization problems by decomposing the original problem into multiple low-dimensional …
Efficient generalized surrogate-assisted evolutionary algorithm for high-dimensional expensive problems
X Cai, L Gao, X Li - IEEE Transactions on Evolutionary …, 2019 - ieeexplore.ieee.org
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 …
A novel evolutionary sampling assisted optimization method for high-dimensional expensive problems
X Wang, GG Wang, B Song, P Wang… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Surrogate-assisted evolutionary algorithms (SAEAs) are promising methods for solving high-
dimensional expensive problems. The basic idea of SAEAs is the integration of nature …
dimensional expensive problems. The basic idea of SAEAs is the integration of nature …
A surrogate-assisted evolutionary algorithm with random feature selection for large-scale expensive problems
When optimizing large-scale problems an evolutionary algorithm typically requires a
substantial number of fitness evaluations to discover a good approximation to the global …
substantial number of fitness evaluations to discover a good approximation to the global …
SAFE: Scale-adaptive fitness evaluation method for expensive optimization problems
The key challenge of expensive optimization problems (EOP) is that evaluating the true
fitness value of the solution is computationally expensive. A common method to deal with …
fitness value of the solution is computationally expensive. A common method to deal with …