A Multi-Objective Genetic Programming Approach with Self-Adaptive alpha Dominance to Uncertain Capacitated Arc Routing Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Wang:2021:CEC,

• author = "Shaolin Wang and Yi Mei and Mengjie Zhang",
• title = "A Multi-Objective Genetic Programming Approach with Self-Adaptive alpha Dominance to Uncertain Capacitated Arc Routing Problem",
• booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2021",
• editor = "Yew-Soon Ong",
• pages = "636--643",
• address = "Krakow, Poland",
• month = "28 " # jun # "-1 " # jul,
• keywords = "genetic algorithms, genetic programming, MOGP, Evolutionary computation, Routing, Tuning, Optimization, Convergence",
• isbn13 = "978-1-7281-8393-0",
• DOI = "doi:10.1109/CEC45853.2021.9504956",
• abstract = "The Uncertain Capacitated Arc Routing Problem (UCARP) has a variety of real-world applications. Genetic Programming Hyper-heuristic (GPHH) is considered a promising technique to handle UCARP. Many scholars have shown the power of GPHH of evolving effective routing policies. However, the size of the evolved routing policies is ignored. Typically, smaller routing policies can have better interpretability and generalisation. Thus, it is necessary to optimise the size along with the effectiveness. The objective selection bias issue arises as the size is much easier to be optimised than effectiveness. The Pareto front is biased to the size gradually during the evolutionary process. To address this issue, we develop an alpha dominance criteria based Multi-Objective GP with a self-adaptive a scheme (aMOGP-sa). The basic idea of the a-dominance criteria is to set tradeoff rates between objectives. For different instances, the search space can be very different. In this case, the self-adaptive a scheme is employed to automatically tuning the a value during the evolutionary process so that we can identify a valid alpha value for different instances. This paper examines the proposed algorithm in eight different problem instances. The experimental results showed that ?MOGP-sa could effectively handle the objective selection bias issue, and evolve much better Pareto front on Hyper-Volume and Inverted Generational Distance than the current state-of-the-art MOGP approach for UCARP in terms of effectiveness and size on all instances. Also, aMOGP-sa can evolve much smaller routing policies than the state-of-art single-objective GPHH without sacrificing effectiveness.",
• notes = "Also known as \cite{9504956}",

}

Genetic Programming entries for Shaolin Wang Yi Mei Mengjie Zhang

Citations