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

Evolutionary algorithms (EAs) have received increasing interests both in the academy and
industry. One main difficulty in applying EAs to real-world applications is that EAs usually …

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

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

Evolutionary algorithms often have to solve optimization problems in the presence of a wide
range of uncertainties. Generally, uncertainties in evolutionary computation can be divided …

It is not unusual that an approximate model is needed for fitness evaluation in evolutionary
computation. In this case, the convergence properties of the evolutionary algorithm are …

Multiobjective evolutionary algorithms (MOEAs) have shown to be effective in solving a wide
range of test problems. However, it is not straightforward to apply MOEAs to complex real …

Fuzzy rule systems have found a wide range of applications in many fields of science and
technology. Traditionally, fuzzy rules are generated from human expert knowledge or human …

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution
of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of …

Approximate models have to be used in evolutionary optimization when the original fitness
function is computationally very expensive. Unfortunately, the convergence property of the …