Machine learning is inherently a multiobjective task. Traditionally, however, either only one
of the objectives is adopted as the cost function or multiple objectives are aggregated to a …

Recently, increasing interest has been shown in applying the concept of Pareto-optimality to
machine learning, particularly inspired by the successful developments in evolutionary multi …

Expensive multi-objective optimization problems can be found in many real-world
applications, where their objective function evaluations involve expensive computations or …

The vast majority of multiobjective evolutionary algorithms presented to date are Pareto-
based. Usually, these algorithms perform well for problems with few (two or three) …

Evolutionary algorithms (EA) have proved to be well suited for optimization problems with
multiple objectives. Due to their inherent parallelism they are able to capture a number of …

The Pareto-based approaches have shown some success in designing multiobjective
evolutionary algorithms (MEAs). Their methods of fitness assignment are mainly from the …

While Pareto-based multiobjective optimization algorithms continue to show effectiveness
for a wide range of practical problems that involve mostly two or three objectives, their …

It is known that the Pareto-based approach is not well suited for optimization problems with a
large number of objectives, even though it is a class of mainstream methods in …

In the real world, it is not uncommon to face an optimization problem with more than three
objectives. Such problems, called many-objective optimization problems (MaOPs), pose …

In multiobjective evolutionary algorithms, most selection operators are based on the
objective values or the approximated objective values. It is arguable that the selection in …