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Reverse Hillclimbing, Genetic Algorithms and the Busy Beaver Problem

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  • Terry Jones
  • Gregory J. E. Rawlins

Abstract

This paper introduces a new analysis tool called {\it reverse hillclimbing}, and demonstrates how it can be used to evaluate the performance of a genetic algorithm. Using reverse hillclimbing, one can calculate the exact probability that hillclimbing will attain some point in a landscape. From this, the expected number of evaluations before the point is found by hillclimbing can be calculated. This figure can be compared to the average number of evaluations done by a genetic algorithm. This procedure is illustrated using the {\it Busy Beaver problem}, an interesting problem of theoretical importance in its own right. At first sight, a genetic algorithm appears to perform very well on this landscape, after examining only a vanishingly small proportion of the space. Closer examination reveals that the number of evaluations it performs to discover an optimal solution compares poorly with even the simples form of hillclimbing. Finally, several other uses for reverse hillclimbing are discussed.

Suggested Citation

  • Terry Jones & Gregory J. E. Rawlins, 1993. "Reverse Hillclimbing, Genetic Algorithms and the Busy Beaver Problem," Working Papers 93-04-024, Santa Fe Institute.
    • Handle: RePEc:wop:safiwp:93-04-024
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    Cited by:

    1. Terry Jones & Stephanie Forrest, 1995. "Genetic Algorithms and Heuristic Search," Working Papers 95-02-021, Santa Fe Institute.
    2. Terry Jones & Stephanie Forrest, 1995. "Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms," Working Papers 95-02-022, Santa Fe Institute.

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