In recent years, there has been a growing research interest in integrating machine learning
techniques into meta-heuristics for solving combinatorial optimization problems. This …

Surrogate-assisted, or meta-model based evolutionary computation uses efficient
computational models, often known as surrogates or meta-models, for approximating the …

Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for
solving expensive optimization problems where only a small number of real fitness …

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

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

Evolutionary algorithms are widely used for solving multiobjective optimization problems but
are often criticized because of a large number of function evaluations needed …

Recently, increasing works have been proposed to drive evolutionary algorithms using
machine-learning models. Usually, the performance of such model-based evolutionary …

Real-world optimization problems typically involve multiple objectives to be optimized
simultaneously under multiple constraints and with respect to several variables. While multi …

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 …

Surrogate-assisted multiobjective evolutionary algorithms (MOEAs) have advanced the field
of computationally expensive optimization, but their progress is often restricted to low …