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When A Genetic Algorithm Outperforms Hill-Climbing
Adam Prügel-Bennett
Theoretical Computer Science
Volume 320,
Number 1,
,
2004.
Abstract A toy optimisation problem is introduced which consists of a fitness
gradient broken up by a series of hurdles. The performance of a
hill-climber and a stochastic hill-climber are computed. These are
compared with the empirically observed performance of a genetic
algorithm (GA) with and without. The hill-climber with a sufficiently
large neighbourhood outperforms the stochastic hill-climber, but is
outperformed by a GA both with and without crossover. The GA with
crossover substantially outperforms all the other heuristics
considered here. The relevance of this result to real world problems
is discussed. [Edit]
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