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Introducing intervention targeting into estimation of distribution algorithms

Published: 26 March 2012 Publication History

Abstract

This paper introduces a new hybrid Genetic Algorithm (GA) crossover approach, Targeted EDA (TEDA), that combines a targeted intervention principle with Estimation of Distribution Algorithms (EDA) to solve optimal control problems. The approach is suited to tasks where the number of interventions used is an important part of solution fitness and includes problems such as cancer chemotherapy scheduling. Fitness Directed Crossover (FDC) is a modified GA crossover method that actively drives the number of selected control interventions towards those of a fitter individual. EDA are able to find fit solutions by discovering patterns within a population of selected individuals. TEDA uses FDC to select a suitable number of interventions to use while using an EDA based approach to select which interventions to set. Results suggest that by combining the two approaches, TEDA is able to outperform both EDA and FDC on a sample optimal control problem.

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Cited By

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  • (2024)Semiparametric Estimation of Distribution Algorithms for Continuous OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.329067028:4(1069-1083)Online publication date: Aug-2024
  • (2015)A Targeted Estimation of Distribution Algorithm Compared to Traditional Methods in Feature SelectionComputational Intelligence10.1007/978-3-319-23392-5_5(83-103)Online publication date: 20-Nov-2015
  • (2012)Targeted EDA adapted for a routing problem with variable length chromosomes2012 IEEE Congress on Evolutionary Computation10.1109/CEC.2012.6256531(1-8)Online publication date: Jun-2012

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cover image ACM Conferences
SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing
March 2012
2179 pages
ISBN:9781450308571
DOI:10.1145/2245276
  • Conference Chairs:
  • Sascha Ossowski,
  • Paola Lecca
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 26 March 2012

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Author Tags

  1. estimation of distribution algorithms
  2. evolutionary algorithms
  3. genetic algorithms
  4. optimal control

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SAC 2012
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SAC 2012: ACM Symposium on Applied Computing
March 26 - 30, 2012
Trento, Italy

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SAC '12 Paper Acceptance Rate 270 of 1,056 submissions, 26%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all
  • (2024)Semiparametric Estimation of Distribution Algorithms for Continuous OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.329067028:4(1069-1083)Online publication date: Aug-2024
  • (2015)A Targeted Estimation of Distribution Algorithm Compared to Traditional Methods in Feature SelectionComputational Intelligence10.1007/978-3-319-23392-5_5(83-103)Online publication date: 20-Nov-2015
  • (2012)Targeted EDA adapted for a routing problem with variable length chromosomes2012 IEEE Congress on Evolutionary Computation10.1109/CEC.2012.6256531(1-8)Online publication date: Jun-2012

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