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 …

In recent years, the development of new algorithms for multiobjective optimization has
considerably grown. A large number of performance indicators has been introduced to …

We consider the usage of evolutionary algorithms for multiobjective programming (MOP), ie
for decision problems with alternatives taken from a real-valued vector space and evaluated …

Application problems have conflicting objectives and constraints, and maximum number of
generations is the most common termination criterion in evolutionary algorithms used for …

Over the last decades, evolutionary algorithms have been extensively used to solve multi-
objective optimization problems. However, the number of required function evaluations is …

The use of multi-objective evolutionary algorithms for solving black-box problems with
multiple conflicting objectives has become an important research area. However, when no …

List of References on Evolutionary Multiobjective Optimization Page 1 List of References on
Evolutionary Multiobjective Optimization Carlos A. Coello Coello ccoello@cs.cinvestav.mx …

Multiobjective evolutionary algorithms evolve a population of solutions through successive
generations toward the Pareto-optimal front (POF). One of the most critical questions faced …

In this paper, two approaches for estimating the generation in which a multi-objective
evolutionary algorithm (MOEA) shows statistically significant signs of convergence are …

A non‐dominance criterion‐based metric that tracks the growth of an archive of non‐
dominated solutions over a few generations is proposed to generate a convergence curve …