Carlos A. Coello-Coello; Julie Greensmith; Natalio Krasnogor; Pietro Liò; Giuseppe Nicosia; Mario Pavone Springer-Verlag Berlin and Heidelberg GmbH & Co. KG (2012) Pehmeäkantinen kirja
Kalyanmoy Deb; Erik Goodman; Carlos A. Coello Coello; Kathrin Klamroth; Kaisa Miettinen; Sanaz Mostaghim; Patrick Reed Springer Nature Switzerland AG (2019) Pehmeäkantinen kirja
Oliver Schuetze (ed.); Carlos A. Coello (ed.); Alexandru-Adrian Tantar (ed.); Emilia Tantar (ed.); Pascal Bouvry (ed.); Mor Springer (2013) Kovakantinen kirja
Oliver Schuetze (ed.); Carlos A. Coello (ed.); Alexandru-Adrian Tantar (ed.); Emilia Tantar (ed.); Pascal Bouvry (ed.); Mor Springer (2015) Pehmeäkantinen kirja
Solving multi-objective problems is an evolving effort, and computer science and other related disciplines have given rise to many powerful deterministic and stochastic techniques for addressing these large-dimensional optimization problems. Evolutionary algorithms are one such generic stochastic approach that has proven to be successful and widely applicable in solving both single-objective and multi-objective problems.
This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems, including test suites with associated performance based on a variety of appropriate metrics, as well as serial and parallel algorithm implementations.
