Self-adaptation in non-elitist evolutionary algorithms on discrete problems with unknown structure
Research output: Contribution to journal › Article › peer-review
Authors
Colleges, School and Institutes
Abstract
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 LEADINGONESk, but
also improves asymptotically on an EA using a state-of-the-art control
mechanism. The structure of this problem depends on a parameter k,
which is 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.
Details
Original language | English |
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Number of pages | 32 |
Journal | IEEE Transactions on Evolutionary Computation |
Publication status | Published - 3 Apr 2020 |
Keywords
- optimization, runtime, evolutionary computation, sociology, statistics, process control, tuning, evolutionary algorithm, self-adaptation, runtime analysis, level-based analysis