Conceptual modeling of evolvable local searches in memetic algorithms using linear genetic programming: a case study on capacitated vehicle routing problem

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{journals/soco/FengOCC16,

• author = "Liang Feng and Yew-Soon Ong and Caishun Chen and Xianshun Chen",
• title = "Conceptual modeling of evolvable local searches in memetic algorithms using linear genetic programming: a case study on capacitated vehicle routing problem",
• journal = "Soft Computing",
• year = "2016",
• number = "9",
• volume = "20",
• pages = "3745--3769",
• keywords = "genetic algorithms, genetic programming, Memetic computation, Individual learning, Linear genetic programming, Adaptive memetic algorithms, Vehicle routing problems",
• bibdate = "2017-05-20",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco20.html#FengOCC16",
• DOI = "doi:10.1007/s00500-015-1971-3",
• abstract = "This paper presents a study on the conceptual modelling of memetic algorithm with evolvable local search in the form of linear programs, self-assembled by linear genetic programming based evolution. In particular, the linear program structure for local search and the associated local search self-assembling process in the lifetime learning process of memetic algorithm are proposed. Results showed that the memetic algorithm with evolvable local search provides a means of creating highly robust, self-configuring and scalable algorithms, thus generating improved or competitive results when benchmarking against several existing adaptive or human-designed state-of-the-art memetic algorithms and meta-heuristics, on a plethora of capacitated vehicle routing problem sets considered.",

}

Genetic Programming entries for Liang Feng Yew-Soon Ong Caishun Chen Xianshun Chen

Citations