06109048

a

06109048 Lara, Adriana Sch\"utze, Oliver Coello Coello, Carlos A. On gradient-based local search to hybridize multi-objective evolutionary algorithms. Tantar, Emilia (ed.) et al., EVOLVE -- a bridge between probability, set oriented numerics and evolutionary computation. Selected papers based on the presentations at the workshop 2011, Bourglinster Castle, Luxembourg, May 25--27, 2011. Berlin: Springer (ISBN 978-3-642-32725-4/hbk; 978-3-642-32726-1/ebook). Studies in Computational Intelligence 447, 305-332 (2013). 2013 Berlin: Springer EN

doi:10.1007/978-3-642-32726-1_9

Summary: Using evolutionary algorithms when solving multi-objective optimization problems (MOPs) has shown remarkable results during the last decade. As a consolidated research area it counts with a number of guidelines and processes; even though, their efficiency is still a big issue which lets room for improvements. In this chapter we explore the use of gradient-based information to increase efficiency on evolutionary methods when dealing with smooth real-valued MOPs. We show the main aspects to be considered when building local search operators using the objective function gradients, and when coupling them with evolutionary algorithms. We present an overview of our current methods with discussion about their convenience for particular kinds of problems.