Hyper-heuristics have emerged as a way to raise the level of generality of search techniques
for computational search problems. This is in contrast to many approaches, which represent …

More than a decade after the first extensive overview on parameter control, we revisit the
field and present a survey of the state-of-the-art. We briefly summarize the development of …

Feature selection is a key pre-processing technique for classification which aims at
removing irrelevant or redundant features from a given dataset. Generally speaking, feature …

When solving constrained multiobjective optimization problems, an important issue is how to
balance convergence, diversity, and feasibility simultaneously. To address this issue, this …

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

The design of evolutionary algorithms has typically been focused on efficiently solving a
single optimization problem at a time. Despite the implicit parallelism of population-based …

Achieving balance between convergence and diversity is a key issue in evolutionary
multiobjective optimization. Most existing methodologies, which have demonstrated their …

Many-objective optimization has posed a great challenge to the classical Pareto dominance-
based multiobjective evolutionary algorithms (MOEAs). In this paper, an evolutionary …

In population-based optimization algorithms (POAs), given an optimization problem, the
quality of the solutions depends heavily on the selection of algorithms, strategies and …

Evolutionary algorithms (EAs) have become one of the most effective techniques for multi-
objective optimization, where a number of variation operators have been developed to …