A genetic algorithm approach to the selection of near-optimal subsets from large sets | Zendy

Philip Whiting | Zendy; P.W. Poon | Zendy; J. N. Carter | Zendy

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A genetic algorithm approach to the selection of near-optimal subsets from large sets

Author(s) -

Philip Whiting,

P.W. Poon,

J. N. Carter

Publication year - 2005

Publication title -

citeseer x (the pennsylvania state university)

Language(s) - English

Resource type - Conference proceedings

ISBN - 1-59593-010-8

DOI - 10.1145/1068009.1068375

Subject(s) - genetic algorithm , sample (material) , selection (genetic algorithm) , property (philosophy) , solvency , computer science , set (abstract data type) , task (project management) , life insurance , algorithm , function (biology) , order (exchange) , mathematical optimization , artificial intelligence , mathematics , machine learning , finance , engineering , philosophy , chemistry , business , systems engineering , epistemology , chromatography , evolutionary biology , market liquidity , actuarial science , economics , biology , programming language

The problem attempted in this paper is to select a sample from a large set where the sample is required to have a particular average property. The problem can be expressed as an optimisation problem where one selects a subset of r objects from a group of n objects and the objective function is the mismatch between the required average property and that of a proposed sample. We test our method on a real-life problem which arises when we model the assets of a life insurance company in order to understand its risk, solvency and/or capital requirements.In this paper we describe a genetic algorithm developed to solve the generic selection task. We demonstrate the algorithm successfully solving our test problem.

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