Expensive optimization problem (EOP) widely exists in various significant real-world
applications. However, EOP requires expensive or even unaffordable costs for evaluating …

Complex continuous optimization problems widely exist nowadays due to the fast
development of the economy and society. Moreover, the technologies like Internet of things …

Multiobjective evolutionary algorithms have incorporated surrogate models in order to
reduce the number of required evaluations to approximate the Pareto front of …

Most evolutionary optimization algorithms assume that the evaluation of the objective and
constraint functions is straightforward. In solving many real-world optimization problems …

Decomposition is a well-known strategy in traditional multiobjective optimization. However,
the decomposition strategy was not widely employed in evolutionary multiobjective …

Surrogate models have shown to be effective in assisting metaheuristic algorithms for
solving computationally expensive complex optimization problems. The effectiveness of …

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 …

Data-driven evolutionary algorithms (DDEAs) aim to utilize data and surrogates to drive
optimization, which is useful and efficient when the objective function of the optimization …

Surrogate-assisted evolutionary algorithms (SAEAs) have become one popular method to
solve complex and computationally expensive optimization problems. However, most …

By efficiently building and exploiting surrogates, data-driven evolutionary algorithms
(DDEAs) can be very helpful in solving expensive and computationally intensive problems …