Genetic algorithm to optimize fitness function with sampling error and its application to financial optimization problem

Masaru Tezuka, Masaharu Munetomo, Kiyoshi Akama, Masahiro Hiji

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper we discuss the optimization problems with noisy fitness function. On financial optimization problems, Monte-Carlo method is commonly used to evaluate the optimization criteria such as value at risk. The evaluation model is often very complex which needs considerable computational overheads. In order to realize efficient optimization of financial problems, we propose a method to decide the number of samples used to estimate the optimization criteria. Selection efficiency proposed in this paper is a index that shows how close the population approaches to the convergence to a good solution. In general, it is difficult to calculate selection efficiency analytically. Thus we also employ bootstrap method to estimate selection efficiency. The resulting algorithm is applied to the optimization of the procurement plan optimization problem. The result shows that Value at Risk of the problem is optimized efficiently by the proposed method.

Original languageEnglish
Title of host publication2006 IEEE Congress on Evolutionary Computation, CEC 2006
Pages81-87
Number of pages7
Publication statusPublished - 2006 Dec 1
Event2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC, Canada
Duration: 2006 Jul 162006 Jul 21

Other

Other2006 IEEE Congress on Evolutionary Computation, CEC 2006
CountryCanada
CityVancouver, BC
Period06/7/1606/7/21

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

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