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Hill climbing on discrete HIFF: exploring the role of DNA transposition in long-term artificial evolution

Published: 07 July 2007 Publication History

Abstract

We show how a random mutation hill climber that does multi-level selection utilizes transposition to escape local optima on the discrete Hierarchical-If-And-Only-If (HIFF) problem. Although transposition is often deleterious to an individual, we outline two population models where recently transposed individuals can survive. In these models, transposed individuals survive selection through cooperation with other individuals. In the multi-population model, individuals were allowed a maturation stage to realize their potential fitness. In the genetic algorithm model, transposition helped maintain genetic diversity even within small populations. However, the results for transposition on the discrete Hierarchical-Exclusive-Or (HXOR) problem were less positive. Unlike HIFF, HXOR does not benefit from random drift. This led us to hypothesize that two conditions necessary for transposition to enhance evolvability are (i) the presence of local optima and (ii) susceptibility to random drift. This hypothesis is supported by further experiments. The findings of this paper suggest that epistasis and large mutations can sustain artificial evolution in the long-term by providing a way for individuals and populations to escape evolutionary dead ends. Paradoxically, epistasis creates local optima and holds a key to its resolution, while deleterious mutations such as transposition enhance evolvability. However, not all large mutations are equal.

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  • (2011)Particle Swarm Optimization and Hill-Climbing Optimized Rough Sets for Modeling Interstate ConflictMilitarized Conflict Modeling Using Computational Intelligence10.1007/978-0-85729-790-7_8(147-164)Online publication date: 26-Jul-2011
  • (2009)Solving Hierarchically Decomposable Problems with the Evolutionary Transition AlgorithmNatural Intelligence for Scheduling, Planning and Packing Problems10.1007/978-3-642-04039-9_5(111-143)Online publication date: 2009
  • (2007)On solving hierarchical problems with top down controlProceedings of the 9th annual conference companion on Genetic and evolutionary computation10.1145/1274000.1274023(2543-2548)Online publication date: 7-Jul-2007

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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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Published: 07 July 2007

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Author Tags

  1. crossing fitness saddles
  2. evolvability
  3. hierarchical test problems
  4. hill climbing

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
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Cited By

View all
  • (2011)Particle Swarm Optimization and Hill-Climbing Optimized Rough Sets for Modeling Interstate ConflictMilitarized Conflict Modeling Using Computational Intelligence10.1007/978-0-85729-790-7_8(147-164)Online publication date: 26-Jul-2011
  • (2009)Solving Hierarchically Decomposable Problems with the Evolutionary Transition AlgorithmNatural Intelligence for Scheduling, Planning and Packing Problems10.1007/978-3-642-04039-9_5(111-143)Online publication date: 2009
  • (2007)On solving hierarchical problems with top down controlProceedings of the 9th annual conference companion on Genetic and evolutionary computation10.1145/1274000.1274023(2543-2548)Online publication date: 7-Jul-2007

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