Analysis of students perceptions of seafaring career in China based on artificial neural network and genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Fei:2015:MPM,

• author = "Jiangang Fei and Jianjun Lu",
• title = "Analysis of students perceptions of seafaring career in China based on artificial neural network and genetic programming",
• journal = "Maritime Policy \& Management",
• year = "2015",
• volume = "42",
• number = "2",
• pages = "111--126",
• keywords = "genetic algorithms, genetic programming, career choice, transport, ANN",
• publisher = "Routledge",
• ISSN = "0308-8839",
• DOI = "doi:10.1080/03088839.2013.873545",
• size = "16 pages",
• abstract = "Research indicates that the worldwide shortage of qualified seafarers has been due to the rapidly increasing world merchant fleet on the one hand and the difficulty of attracting and retaining people in the industry on the other. The paper argues that in the context of the Chinese shipping industry, the high wastage is one of the major contributors to the shortage. The aims of the research are to examine the motives of students studying nautical courses; identify the key factors affecting nautical students to choose seafaring career, and to predict students career choice behavior. The research finds that (a) those who had a clear sense of their future career when enrolled in the nautical studies tend to choose a seafaring career and remain active for a longer period of time; (b) high wage is the most important factor affecting students’ decisions to choose seafaring as a career; (c) prolonged separation from family, job opportunities ashore, and concern about children growth and education are among the top three factors for students not to choose a seafaring career or to quit it early; and (d) nautical students career choice behaviors can be predicted through the use of artificial neural network (ANN) and genetic programming (GP) with five rules being generated. This novel methodological approach, a combination of ANN and GP, has been proven to be effective in analysing complex variables and in generating rules.",
• notes = "p126 'the use of ANN and GP .. has been proven to be effective inpredicting the seafaring choice behaviors'

  www.tandfonline.com/journals/tmpm20",

}

Genetic Programming entries for Jiangang Fei Jian-Jun Lu

Citations