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Varying environments can speed up evolution
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Edited by Curtis G. Callan, Jr., Princeton University, Princeton, NJ, and approved June 19, 2007 (received for review December 28, 2006)

Abstract
Simulations of biological evolution, in which computers are used to evolve systems toward a goal, often require many generations to achieve even simple goals. It is therefore of interest to look for generic ways, compatible with natural conditions, in which evolution in simulations can be speeded. Here, we study the impact of temporally varying goals on the speed of evolution, defined as the number of generations needed for an initially random population to achieve a given goal. Using computer simulations, we find that evolution toward goals that change over time can, in certain cases, dramatically speed up evolution compared with evolution toward a fixed goal. The highest speedup is found under modularly varying goals, in which goals change over time such that each new goal shares some of the subproblems with the previous goal. The speedup increases with the complexity of the goal: the harder the problem, the larger the speedup. Modularly varying goals seem to push populations away from local fitness maxima, and guide them toward evolvable and modular solutions. This study suggests that varying environments might significantly contribute to the speed of natural evolution. In addition, it suggests a way to accelerate optimization algorithms and improve evolutionary approaches in engineering.
Footnotes
- *To whom correspondence should be addressed. E-mail: urialon{at}weizmann.ac.il
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Author contributions: N.K., E.N., and U.A. designed research, performed research, analyzed data, and wrote the paper.
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The authors declare no conflict of interest.
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This article is a PNAS Direct Submission.
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This article contains supporting information online at www.pnas.org/cgi/content/full/0611630104/DC1.
- Abbreviations:
- Gn,
- goal n;
- MVG,
- modularly varying goals;
- NAND,
- NOT AND;
- RVG,
- randomly varying goals;
- XOR,
- exclusive-OR.
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Freely available online through the PNAS open access option.
- © 2007 by The National Academy of Sciences of the USA