1,157 research outputs found
Algorithm Portfolio for Individual-based Surrogate-Assisted Evolutionary Algorithms
Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation
tools for computationally expensive problems (CEPs). However, a randomly
selected algorithm may fail in solving unknown problems due to no free lunch
theorems, and it will cause more computational resource if we re-run the
algorithm or try other algorithms to get a much solution, which is more serious
in CEPs. In this paper, we consider an algorithm portfolio for SAEAs to reduce
the risk of choosing an inappropriate algorithm for CEPs. We propose two
portfolio frameworks for very expensive problems in which the maximal number of
fitness evaluations is only 5 times of the problem's dimension. One framework
named Par-IBSAEA runs all algorithm candidates in parallel and a more
sophisticated framework named UCB-IBSAEA employs the Upper Confidence Bound
(UCB) policy from reinforcement learning to help select the most appropriate
algorithm at each iteration. An effective reward definition is proposed for the
UCB policy. We consider three state-of-the-art individual-based SAEAs on
different problems and compare them to the portfolios built from their
instances on several benchmark problems given limited computation budgets. Our
experimental studies demonstrate that our proposed portfolio frameworks
significantly outperform any single algorithm on the set of benchmark problems
Developmental constraints, innovations and robustness
During my PhD, I have been working on Evo-Devo patterns (especially the debate around the
hourglass model) in transcriptomes, with an emphasis on adaptation. I have characterized
patterns in model organisms in terms of constraints and especially in terms of positive selection.
I found that the phylotypic stage (a stage in mid-embryonic development) is an evolutionary
lockdown, with stronger purifying selection and less positive selection than other stages in
terms of the evolution of protein sequences and of regulatory elements. To study the adaptive
evolution of gene regulation during development, I have developed a machine leaning based
in silico mutagenesis approach to detect positive selection on regulatory elements.
In addition to transcriptome evolution, I have been working on the tension between precision
and stochasticity of gene expression during development. More precisely, I have shown that
expression noise follows an hourglass pattern, with lower noise at the phylotypic stage. This
pattern can be explained by stronger histone modification mediated noise control at this stage.
In addition, I propose that histone modifications contribute to mutational robustness in
regulatory elements, and thus to conserved expression levels. These results provide insight into
the role of robustness in the phenotypic and genetic patterns of evolutionary conservation in
animal developmen
Distributed Multi-Task Relationship Learning
Multi-task learning aims to learn multiple tasks jointly by exploiting their
relatedness to improve the generalization performance for each task.
Traditionally, to perform multi-task learning, one needs to centralize data
from all the tasks to a single machine. However, in many real-world
applications, data of different tasks may be geo-distributed over different
local machines. Due to heavy communication caused by transmitting the data and
the issue of data privacy and security, it is impossible to send data of
different task to a master machine to perform multi-task learning. Therefore,
in this paper, we propose a distributed multi-task learning framework that
simultaneously learns predictive models for each task as well as task
relationships between tasks alternatingly in the parameter server paradigm. In
our framework, we first offer a general dual form for a family of regularized
multi-task relationship learning methods. Subsequently, we propose a
communication-efficient primal-dual distributed optimization algorithm to solve
the dual problem by carefully designing local subproblems to make the dual
problem decomposable. Moreover, we provide a theoretical convergence analysis
for the proposed algorithm, which is specific for distributed multi-task
relationship learning. We conduct extensive experiments on both synthetic and
real-world datasets to evaluate our proposed framework in terms of
effectiveness and convergence.Comment: To appear in KDD 201
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