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ExGA II: an improved exonic genetic algorithm for the multiple knapsack problem

Published: 07 July 2007 Publication History

Abstract

ExGA I, a previously presented genetic algorithm, successfully solved numerous instances of the multiple knapsack problem (MKS) by employing an adaptive repair function that made use of the algorithm's modular encoding. Here we present ExGA II, an extension of ExGA I that implements additional features which allow the algorithm to perform more reliably across a larger set of benchmark problems. In addition to some basic modifications of the algorithm's framework, more specific extensions include the use of a biased mutation operator and adaptive control sequences which are used to guide the repair procedure. The success rate of ExGA II is superior to its predecessor, and other algorithms in the literature, without an overall increase in the number of function evaluations required to reach the global optimum. In fact, the new algorithm exhibits a significant reduction in the number of function evaluations required for the largest problems investigated. We also address the computational cost of using a repair function and show that the algorithm remains highly competitive when this cost is accounted for.

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Cited By

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  • (2011)A simple improvement heuristic for attributed grammatical evolution with lookahead to solve the multiple knapsack problemProceedings of the 5th international conference on Convergence and hybrid information technology10.5555/2045005.2045041(274-281)Online publication date: 22-Sep-2011
  • (2011)Degeneracy Reduction or Duplicate Elimination? An Analysis on the Performance of Attributed Grammatical Evolution with Lookahead to Solve the Multiple Knapsack ProblemNature Inspired Cooperative Strategies for Optimization (NICSO 2011)10.1007/978-3-642-24094-2_18(247-266)Online publication date: 2011

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cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958
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: 07 July 2007

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Author Tags

  1. genetic algorithms
  2. molecular genetics
  3. multiple knapsack problem
  4. repair functions

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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View all
  • (2011)A simple improvement heuristic for attributed grammatical evolution with lookahead to solve the multiple knapsack problemProceedings of the 5th international conference on Convergence and hybrid information technology10.5555/2045005.2045041(274-281)Online publication date: 22-Sep-2011
  • (2011)Degeneracy Reduction or Duplicate Elimination? An Analysis on the Performance of Attributed Grammatical Evolution with Lookahead to Solve the Multiple Knapsack ProblemNature Inspired Cooperative Strategies for Optimization (NICSO 2011)10.1007/978-3-642-24094-2_18(247-266)Online publication date: 2011

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