In almost no other field of computer science, the idea of using bio-inspired search paradigms
has been so useful as in solving multiobjective optimization problems. The idea of using a …

A multiobjective optimization problem involves several conflicting objectives and has a set of
Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary …

For over 25 years, most multi-objective evolutionary algorithms (MOEAs) have adopted
selection criteria based on Pareto dominance. However, the performance of Pareto-based …

In the field of evolutionary multi-criterion optimization, the hypervolume indicator is the only
single set quality measure that is known to be strictly monotonic with regard to Pareto …

When attempting to better understand the strengths and weaknesses of an algorithm, it is
important to have a strong understanding of the problem at hand. This is true for the field of …

The hypervolume measure (or S metric) is a frequently applied quality measure for
comparing the results of evolutionary multiobjective optimisation algorithms (EMOA). The …

We present an algorithm for calculating hypervolume exactly, the Hypervolume by Slicing
Objectives (HSO) algorithm, that is faster than any that has previously been published. HSO …

We describe a new algorithm WFG for calculating hypervolume exactly. WFG is based on
the recently-described observation that the exclusive hypervolume of a point p relative to a …

Randomized search heuristics such as evolutionary algorithms, genetic algorithms,
evolution strategies, ant colony and particle swarm optimization turn out to be highly …

The goal of multiobjective optimization is to find a set of best compromise solutions for
typically conflicting objectives. Due to the complex nature of most real-life problems, only an …