An Adaptive Estimation of Distribution Algorithm for Multipolicy Insurance Investment Planning

Insurance has been increasingly realized as an important way of investment and risk aversion. Fruitful insurance products are launched by insurers, but there is little research on how to make a proper insurance investment plan for a specific policyholder given different kinds of policies. In this pa...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on evolutionary computation Ročník 23; číslo 1; s. 1 - 14
Hlavní autoři: Shi, Wen, Chen, Wei-Neng, Lin, Ying, Gu, Tianlong, Kwong, Sam, Zhang, Jun
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.02.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptation models
Adaptive algorithms
Biological system modeling
Constraint modelling
Data-driven
Deduction
endowment insurance
Estimation
estimation of distribution algorithm (EDA)
hospitalization insurances
Insurance
Investment
Markets
Mathematical model
mixed-variable optimization
Optimization
Policies
ISSN:1089-778X, 1941-0026
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Insurance has been increasingly realized as an important way of investment and risk aversion. Fruitful insurance products are launched by insurers, but there is little research on how to make a proper insurance investment plan for a specific policyholder given different kinds of policies. In this paper, we aim to propose a practical approach to multipolicy insurance investment planning with a data-driven model and an estimation of distribution algorithm (EDA). First, by making use of the insurance data accumulated in the modern financial market, an optimization model about how to choose endowment and hospitalization policies is built to maximize the yearly profit of insurance investment. With the model parameters set according to the real data from insurance market, the resulting plan is practical and individualized. Second, as the optimal solution cannot be achieved by mathematical deduction under this data-driven model, an EDA is introduced. To adapt the EDA for the considered problem, the proposed EDA is mixed with both the continuous and discrete probability distribution models to handle different kinds of variables. In addition, an adaptive scheme for choosing suitable distribution models and an efficient constraint handling strategy are proposed. Experiments under different conditions confirm the effectiveness and efficiency of the proposed model and method.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2017.2782571