Evolutionary Multi-Objective Optimization is an expanding field of research. This book brings a collection of papers with some of the most recent advances in this field.

This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.

This book gives a unique and detailed account of the current status of research and applications in the field of multiobjective optimization.

This paper investigates a new evolutionary algorithm-based multiobjective optimization procedure that builds upon the Tchebycheff method. By embedding a beneficial seeding approach, the efficiency of the algorithm is expectedly enhanced.

This book covers the most recent advances in the field of evolutionary multiobjective optimization.

This book focuses on the field of surrogate-model-based multiobjective evolutionary optimization.

The primary motivation of this book is to provide a comprehensive introduction on the design and application of evolutionary algorithms for multi-objective optimization in the presence of uncertainties.

Keywords: Multiobjective Optimization, Evolutionary computation.

In this book, the various features of multi-objective evolutionary algorithms (MOEAs) are presented in an innovative and unique fashion, with detailed customized forms suggested for a variety of applications.

Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics.