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Introducing a vertical viral infection method for solving problems with evolutionary programming

Published: 28 March 2008 Publication History

Abstract

Compared to standard search algorithms, which can become unwieldy as the solution space expands, genetic algorithms can often provide a near-optimal solution in fraction of the time. However, genetic algorithms add additional complications, such as stalling at local maxima or minima in the solution space, that can sometimes return an inferior candidate solution.
Many resolutions to these problems have been proposed to introduce variation in candidate solutions in order to provide a nudge to genetic algorithms when they become stuck. An alternative that has gained acceptance in recent years is "viral infection," which provides a simple method of introducing new material into the solution space. In contrast to mutation, which introduces new information vertically, that is, in subsequent generations, viral infection allows the horizontal propagation of new information into the current host population through infection of members of the population by a "virus."
The approach to viral infection presented in this paper departs dramatically from previously published implementations by incorporating viral infection into the genetic algorithms vertically, which can produce better candidate solutions in less time. This paper will examine several variations to the alternative viral infection method described and will compare and contrast their results on a simple problem space.

References

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Darwin, Charles, "On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life," 1859, John Murray (publisher), London, England.
[2]
Fogel, David B., "Evolutionary Computation, Toward a New Philosophy of Machine Intelligence, Second Edition," 2000, IEEE Press, Institute of Electrical and Electronics Engineers, New York, N.Y.
[3]
Russel, Stuart and Norvig, Peter, "Artificial Intelligence, A Modern Approach," 1995, Prentice Hall Inc., Upper Saddle River, N.J.
[4]
Kanoh, Hitoshi, et al. "Solving Constraint Satisfaction Problems by a Genetic Algorithm Using Viral Infection" in Proceedings of the IEEE International Joint Symposium on Intelligence and Systems, pp. 67--73, Institute of Electrical and Electronics Engineers, Boston, MA.
[5]
Kubota, Naoyuki and Fukuda, Toshio, "Schema Representation in Virus-Evolutionary Genetic Algorithm for Knapsack Problem" in Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, pp. 834--839, Institute of Electrical and Electronics Engineers, Boston, MA.
[6]
Raidl, Gunther, "An Improved Genetic Algorithm for the Multiconstrained 0--1 Knapsack Problem" in Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, pp. 207--212, Institute of Electrical and Electronics Engineers, Boston, MA.
[7]
Kubota, Hitoshi, et. al., "Evolutionary Transition on Virus-Evolutionary Genetic Algorithm" in Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, pp. 291--302, Institute of Electrical and Electronics Engineers, Boston, MA.

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ACMSE '08: Proceedings of the 46th annual ACM Southeast Conference
March 2008
548 pages
ISBN:9781605581057
DOI:10.1145/1593105
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 March 2008

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Author Tags

  1. GAVI
  2. evolutionary programming
  3. genetic algorithms
  4. viral infection

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ACM SE08
ACM SE08: ACM Southeast Regional Conference
March 28 - 29, 2008
Alabama, Auburn

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Overall Acceptance Rate 502 of 1,023 submissions, 49%

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