Introducing MONEDA: scalable multiobjective optimization with a neural estimation of distribution algorithm

In this paper we explore the model–building issue of multiobjective optimization estimation of distribution algorithms. We argue that model–building has some characteristics that differentiate it from other machine learning tasks. A novel algorithm called multiobjective neural estimation of distribution algorithm (MONEDA) is proposed to meet those characteristics. This algorithm uses a custom version of the growing neural gas (GNG) network specially meant for the model–building task. As part of this work, MONEDA is assessed with regard to other classical and state–of–the–art evolutionary multiobjective optimizers when solving some community accepted test problems. Categories and Subject Descriptors I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods and Search; I.2.m [Artificial Intelligence]: Evolutionary Computing and Genetic Algorithms—Multiobjective Evolutionary Algorithms General Terms Algorithms, Experimentation, Performance Keywords Multiobjective...

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Luis Martí, Jesús García, Antonio Berlanga, José Manuel Molina
• Keywords: Distribution Algorithms, GECCO 2008, Growing Neural Gas, Multiobjective Optimization, Optimization