1,448 research outputs found
On the Easiest and Hardest Fitness Functions
The hardness of fitness functions is an important research topic in the field
of evolutionary computation. In theory, the study can help understanding the
ability of evolutionary algorithms. In practice, the study may provide a
guideline to the design of benchmarks. The aim of this paper is to answer the
following research questions: Given a fitness function class, which functions
are the easiest with respect to an evolutionary algorithm? Which are the
hardest? How are these functions constructed? The paper provides theoretical
answers to these questions. The easiest and hardest fitness functions are
constructed for an elitist (1+1) evolutionary algorithm to maximise a class of
fitness functions with the same optima. It is demonstrated that the unimodal
functions are the easiest and deceptive functions are the hardest in terms of
the time-fitness landscape. The paper also reveals that the easiest fitness
function to one algorithm may become the hardest to another algorithm, and vice
versa
Parametric Alignment of Drosophila Genomes
The classic algorithms of Needleman--Wunsch and Smith--Waterman find a
maximum a posteriori probability alignment for a pair hidden Markov model
(PHMM). In order to process large genomes that have undergone complex genome
rearrangements, almost all existing whole genome alignment methods apply fast
heuristics to divide genomes into small pieces which are suitable for
Needleman--Wunsch alignment. In these alignment methods, it is standard
practice to fix the parameters and to produce a single alignment for subsequent
analysis by biologists.
Our main result is the construction of a whole genome parametric alignment of
Drosophila melanogaster and Drosophila pseudoobscura. Parametric alignment
resolves the issue of robustness to changes in parameters by finding all
optimal alignments for all possible parameters in a PHMM. Our alignment draws
on existing heuristics for dividing whole genomes into small pieces for
alignment, and it relies on advances we have made in computing convex polytopes
that allow us to parametrically align non-coding regions using biologically
realistic models. We demonstrate the utility of our parametric alignment for
biological inference by showing that cis-regulatory elements are more conserved
between Drosophila melanogaster and Drosophila pseudoobscura than previously
thought. We also show how whole genome parametric alignment can be used to
quantitatively assess the dependence of branch length estimates on alignment
parameters.
The alignment polytopes, software, and supplementary material can be
downloaded at http://bio.math.berkeley.edu/parametric/.Comment: 19 pages, 3 figure
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