9,508 research outputs found
A Runtime Analysis of Parallel Evolutionary Algorithms in Dynamic Optimization
A simple island model with λλ islands and migration occurring after every ττ iterations is studied on the dynamic fitness function Maze. This model is equivalent to a (1+λ)(1+λ) EA if τ=1τ=1 , i. e., migration occurs during every iteration. It is proved that even for an increased offspring population size up to λ=O(n1−ϵ)λ=O(n1−ϵ) , the (1+λ)(1+λ) EA is still not able to track the optimum of Maze. If the migration interval is chosen carefully, the algorithm is able to track the optimum even for logarithmic λλ . The relationship of τ,λτ,λ , and the ability of the island model to track the optimum is then investigated more closely. Finally, experiments are performed to supplement the asymptotic results, and investigate the impact of the migration topology
Optimal Parameter Choices Through Self-Adjustment: Applying the 1/5-th Rule in Discrete Settings
While evolutionary algorithms are known to be very successful for a broad
range of applications, the algorithm designer is often left with many
algorithmic choices, for example, the size of the population, the mutation
rates, and the crossover rates of the algorithm. These parameters are known to
have a crucial influence on the optimization time, and thus need to be chosen
carefully, a task that often requires substantial efforts. Moreover, the
optimal parameters can change during the optimization process. It is therefore
of great interest to design mechanisms that dynamically choose best-possible
parameters. An example for such an update mechanism is the one-fifth success
rule for step-size adaption in evolutionary strategies. While in continuous
domains this principle is well understood also from a mathematical point of
view, no comparable theory is available for problems in discrete domains.
In this work we show that the one-fifth success rule can be effective also in
discrete settings. We regard the ~GA proposed in
[Doerr/Doerr/Ebel: From black-box complexity to designing new genetic
algorithms, TCS 2015]. We prove that if its population size is chosen according
to the one-fifth success rule then the expected optimization time on
\textsc{OneMax} is linear. This is better than what \emph{any} static
population size can achieve and is asymptotically optimal also among
all adaptive parameter choices.Comment: This is the full version of a paper that is to appear at GECCO 201
Runtime Analysis for Self-adaptive Mutation Rates
We propose and analyze a self-adaptive version of the
evolutionary algorithm in which the current mutation rate is part of the
individual and thus also subject to mutation. A rigorous runtime analysis on
the OneMax benchmark function reveals that a simple local mutation scheme for
the rate leads to an expected optimization time (number of fitness evaluations)
of when is at least for
some constant . For all values of , this
performance is asymptotically best possible among all -parallel
mutation-based unbiased black-box algorithms.
Our result shows that self-adaptation in evolutionary computation can find
complex optimal parameter settings on the fly. At the same time, it proves that
a relatively complicated self-adjusting scheme for the mutation rate proposed
by Doerr, Gie{\ss}en, Witt, and Yang~(GECCO~2017) can be replaced by our simple
endogenous scheme.
On the technical side, the paper contributes new tools for the analysis of
two-dimensional drift processes arising in the analysis of dynamic parameter
choices in EAs, including bounds on occupation probabilities in processes with
non-constant drift
Towards a Theory-Guided Benchmarking Suite for Discrete Black-Box Optimization Heuristics: Profiling EA Variants on OneMax and LeadingOnes
Theoretical and empirical research on evolutionary computation methods
complement each other by providing two fundamentally different approaches
towards a better understanding of black-box optimization heuristics. In
discrete optimization, both streams developed rather independently of each
other, but we observe today an increasing interest in reconciling these two
sub-branches. In continuous optimization, the COCO (COmparing Continuous
Optimisers) benchmarking suite has established itself as an important platform
that theoreticians and practitioners use to exchange research ideas and
questions. No widely accepted equivalent exists in the research domain of
discrete black-box optimization.
Marking an important step towards filling this gap, we adjust the COCO
software to pseudo-Boolean optimization problems, and obtain from this a
benchmarking environment that allows a fine-grained empirical analysis of
discrete black-box heuristics. In this documentation we demonstrate how this
test bed can be used to profile the performance of evolutionary algorithms.
More concretely, we study the optimization behavior of several EA
variants on the two benchmark problems OneMax and LeadingOnes. This comparison
motivates a refined analysis for the optimization time of the EA
on LeadingOnes
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