Certain problems have characteristics that present difficulties for metaheuristics: their objective function may be either prohibitively expensive, or they may only give a partial ordering over the solutions, lacking a suitable gradient to guide the search. In such cases, it may be more efficient to use a surrogate fitness function to replace or supplement the objective function. This paper provides a broad perspective on surrogate fitness functions, described in the form of a metaheuristic design pattern
This paper presents a very simple surrogate optimization method - a Tolerance-based Surrogate Method...
This work presents enhancements to a surrogate-assisted evolutionary optimization framework proposed...
In real-world problems with candidate solutions that are very expensive to evaluate, Surrogate Model...
Certain problems have characteristics that present difficulties for metaheuristics: their objective ...
Metaheuristics are randomised search algorithms that are effective at finding "good enough" solution...
Metaheuristic search algorithms look for solutions that either max-imise or minimise a set of object...
Metaheuristics are randomised search algorithms that are effective at finding ”good enough” solution...
Surrogate fitness functions are a popular technique for speeding up metaheuristics, replacing calls ...
To deal with complex optimization problems plagued with computationally expensive fitness functions,...
Le MN, Ong YS, Menzel S, Jin Y, Sendhoff B. Evolution by Adapting Surrogates. Evolutionary Computati...
This paper is concerned with an experimental evaluation of coevolutionary optimization techniques, w...
Surrogate-Assisted Memetic Algorithm (SAMA) is a hybrid evolutionary algorithm, particularly a memet...
Today and always, human progress has been linked, among other aspects, to the capacity of facing pro...
This paper is concerned with an experimental evaluation of coevolutionary optimization techniques, w...
Surrogate-assisted, or meta-model based evolutionary computation uses efficient computational models...
This paper presents a very simple surrogate optimization method - a Tolerance-based Surrogate Method...
This work presents enhancements to a surrogate-assisted evolutionary optimization framework proposed...
In real-world problems with candidate solutions that are very expensive to evaluate, Surrogate Model...
Certain problems have characteristics that present difficulties for metaheuristics: their objective ...
Metaheuristics are randomised search algorithms that are effective at finding "good enough" solution...
Metaheuristic search algorithms look for solutions that either max-imise or minimise a set of object...
Metaheuristics are randomised search algorithms that are effective at finding ”good enough” solution...
Surrogate fitness functions are a popular technique for speeding up metaheuristics, replacing calls ...
To deal with complex optimization problems plagued with computationally expensive fitness functions,...
Le MN, Ong YS, Menzel S, Jin Y, Sendhoff B. Evolution by Adapting Surrogates. Evolutionary Computati...
This paper is concerned with an experimental evaluation of coevolutionary optimization techniques, w...
Surrogate-Assisted Memetic Algorithm (SAMA) is a hybrid evolutionary algorithm, particularly a memet...
Today and always, human progress has been linked, among other aspects, to the capacity of facing pro...
This paper is concerned with an experimental evaluation of coevolutionary optimization techniques, w...
Surrogate-assisted, or meta-model based evolutionary computation uses efficient computational models...
This paper presents a very simple surrogate optimization method - a Tolerance-based Surrogate Method...
This work presents enhancements to a surrogate-assisted evolutionary optimization framework proposed...
In real-world problems with candidate solutions that are very expensive to evaluate, Surrogate Model...