2 research outputs found
Self-adaptation in non-elitist evolutionary algorithms on discrete problems with unknown structure
A key challenge to make effective use of evolutionary algorithms is to choose
appropriate settings for their parameters. However, the appropriate parameter
setting generally depends on the structure of the optimisation problem, which
is often unknown to the user. Non-deterministic parameter control mechanisms
adjust parameters using information obtained from the evolutionary process.
Self-adaptation -- where parameter settings are encoded in the chromosomes of
individuals and evolve through mutation and crossover -- is a popular parameter
control mechanism in evolutionary strategies. However, there is little
theoretical evidence that self-adaptation is effective, and self-adaptation has
largely been ignored by the discrete evolutionary computation community.
Here we show through a theoretical runtime analysis that a non-elitist,
discrete evolutionary algorithm which self-adapts its mutation rate not only
outperforms EAs which use static mutation rates on \leadingones, but also
improves asymptotically on an EA using a state-of-the-art control mechanism.
The structure of this problem depends on a parameter , which is \emph{a
priori} unknown to the algorithm, and which is needed to appropriately set a
fixed mutation rate. The self-adaptive EA achieves the same asymptotic runtime
as if this parameter was known to the algorithm beforehand, which is an
asymptotic speedup for this problem compared to all other EAs previously
studied. An experimental study of how the mutation-rates evolve show that they
respond adequately to a diverse range of problem structures.
These results suggest that self-adaptation should be adopted more broadly as
a parameter control mechanism in discrete, non-elitist evolutionary algorithms.Comment: To appear in IEEE Transactions of Evolutionary Computatio