4,994 research outputs found
A modified bats echolocation-based algorithm for solving constrained optimisation problems
A modified adaptive bats sonar algorithm (MABSA) is presented that utilises the concept of echolocation of a colony of bats to find prey. The proposed algorithm is applied to solve the constrained optimisation problems coupled with penalty function method as constraint handling technique. The performance of the algorithm is verified through rigorous tests with four constrained optimisation benchmark test functions. The acquired results show that the proposed algorithm performs better to find optimum solution in terms of accuracy and convergence speed. The statistical results of MABSA to solve all the test functions also has been compared with the results from several existing algorithms taken from literature on similar test functions. The comparative study has shown that MABSA outperforms other establish algorithms, and thus, it can be an efficient alternative method in the solving constrained optimisation problems
Quantifying Differential Privacy in Continuous Data Release under Temporal Correlations
Differential Privacy (DP) has received increasing attention as a rigorous
privacy framework. Many existing studies employ traditional DP mechanisms
(e.g., the Laplace mechanism) as primitives to continuously release private
data for protecting privacy at each time point (i.e., event-level privacy),
which assume that the data at different time points are independent, or that
adversaries do not have knowledge of correlation between data. However,
continuously generated data tend to be temporally correlated, and such
correlations can be acquired by adversaries. In this paper, we investigate the
potential privacy loss of a traditional DP mechanism under temporal
correlations. First, we analyze the privacy leakage of a DP mechanism under
temporal correlation that can be modeled using Markov Chain. Our analysis
reveals that, the event-level privacy loss of a DP mechanism may
\textit{increase over time}. We call the unexpected privacy loss
\textit{temporal privacy leakage} (TPL). Although TPL may increase over time,
we find that its supremum may exist in some cases. Second, we design efficient
algorithms for calculating TPL. Third, we propose data releasing mechanisms
that convert any existing DP mechanism into one against TPL. Experiments
confirm that our approach is efficient and effective.Comment: accepted in TKDE special issue "Best of ICDE 2017". arXiv admin note:
substantial text overlap with arXiv:1610.0754
Biochemical systems identification by a random drift particle swarm optimization approach
BACKGROUND: Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for signalling pathways. The problem is stated as a data-driven nonlinear regression problem, which is converted into a nonlinear programming problem with many nonlinear differential and algebraic constraints. Due to the typical ill conditioning and multimodality nature of the problem, it is in general difficult for gradient-based local optimization methods to obtain satisfactory solutions. To surmount this limitation, many stochastic optimization methods have been employed to find the global solution of the problem. RESULTS: This paper presents an effective search strategy for a particle swarm optimization (PSO) algorithm that enhances the ability of the algorithm for estimating the parameters of complex dynamic biochemical pathways. The proposed algorithm is a new variant of random drift particle swarm optimization (RDPSO), which is used to solve the above mentioned inverse problem and compared with other well known stochastic optimization methods. Two case studies on estimating the parameters of two nonlinear biochemical dynamic models have been taken as benchmarks, under both the noise-free and noisy simulation data scenarios. CONCLUSIONS: The experimental results show that the novel variant of RDPSO algorithm is able to successfully solve the problem and obtain solutions of better quality than other global optimization methods used for finding the solution to the inverse problems in this study
Multiple time–scale dynamics of stage structured populations and derivative–free optimization
The parent-progeny (adult fish–juvenile) relationship is central to understanding the dynamics of fish populations. Management and harvest decisions are based on the assumption of a stock-recruitment function that relates the number of adults to their progeny. Multi-stage population dynamic models provide a modelling framework for understanding this relationship, since they describe the dynamics of fish in several life stages (such as adults, eggs, larvae, and juveniles). Biological processes at various life stages usually evolve at distinct time scales. This thesis contains three papers, which address challenges in modelling and parameter estimation for multiple time-scale dynamics of stage structured populations. A major question is, whether a multi-stage population dynamic model supports the assumption of a stock-recruitment function. In the first paper, we address the parent-progeny relationship admitted by slow-fast systems of differential equations that model the dynamics of a fish population with two stages. We introduce a slow-fast population dynamic model which replicates several well-known stock-recruitment functions. Traditionally, the dynamics of fish populations are described by difference equations. In the second paper, discrete time models for several life stages are formulated. We demonstrate that a multi-stage model may not admit a stock-recruitment function. Sufficient conditions for the validity of two hypotheses about the existence and structure of a parent-progeny function are established. Parameters in population dynamic models can be estimated by minimizing a function of the solution of the ordinary differential equations and available data. Efficient and accurate methods for the solution of differential equations usually evaluate conditional statements. In this case, the objective function may be noisy, instead of continuously differentiable. Furthermore, an algorithm which is used to evaluate the objective function may unexpectedly fail to return a (plausible) value. Then, the optimization problem includes constraints which are only implicitly stated and hidden from the problem formulation. We demonstrate that derivative-free optimization methods find sufficiently accurate solutions for the challenging optimization problems. In the third paper, we compare the performances of several derivative-free methods for a set of optimization problems. We find that a derivative-free trust-region method is most robust to the choice of the initial iterate, but is in general outperformed by direct search methods. Additional numerical simulations in the thesis reveal that direct search methods which approximate a gradient or Hessian find the most accurate solutions. We observe that the optimization problems considered in this thesis are more challenging than a set of noisy benchmark problems. The thesis includes scientific contributions in addition to the results from the three papers
Gene regulatory network modelling with evolutionary algorithms -an integrative approach
Building models for gene regulation has been an important aim of Systems Biology over the past years, driven by the large amount of gene expression data that has become available. Models represent regulatory interactions between genes and transcription factors and can provide better understanding of biological processes, and means of simulating both natural and perturbed systems (e.g. those associated with disease). Gene regulatory network
(GRN) quantitative modelling is still limited, however, due to data issues such as noise and restricted length of time series, typically used for GRN reverse engineering. These issues create an under-determination problem, with many models possibly fitting the data. However,
large amounts of other types of biological data and knowledge are available, such as cross-platform measurements, knockout experiments, annotations, binding site affinities for transcription factors and so on. It has been postulated that integration of these can improve
model quality obtained, by facilitating further filtering of possible models. However, integration is not straightforward, as the different types of data can provide contradictory information, and are intrinsically noisy, hence large scale integration has not been fully
explored, to date. Here, we present an integrative parallel framework for GRN modelling, which employs
evolutionary computation and different types of data to enhance model inference. Integration is performed at different levels. (i) An analysis of cross-platform integration of time series microarray data, discussing the effects on the resulting models and exploring crossplatform
normalisation techniques, is presented. This shows that time-course data integration is possible, and results in models more robust to noise and parameter perturbation, as
well as reduced noise over-fitting. (ii) Other types of measurements and knowledge, such as knock-out experiments, annotated transcription factors, binding site affinities and promoter sequences are integrated within the evolutionary framework to obtain more plausible GRN models. This is performed by customising initialisation, mutation and evaluation of candidate model solutions. The different data types are investigated and both qualitative and
quantitative improvements are obtained. Results suggest that caution is needed in order to obtain improved models from combined data, and the case study presented here provides
an example of how this can be achieved. Furthermore, (iii), RNA-seq data is studied in comparison to microarray experiments, to identify overlapping features and possibilities of integration within the framework. The extension of the framework to this data type is
straightforward and qualitative improvements are obtained when combining predicted interactions
from single-channel and RNA-seq datasets
Self-organization of signal transduction
We propose a model of parameter learning for signal transduction, where the
objective function is defined by signal transmission efficiency. We apply this
to learn kinetic rates as a form of evolutionary learning, and look for
parameters which satisfy the objective. This is a novel approach compared to
the usual technique of adjusting parameters only on the basis of experimental
data. The resulting model is self-organizing, i.e. perturbations in protein
concentrations or changes in extracellular signaling will automatically lead to
adaptation. We systematically perturb protein concentrations and observe the
response of the system. We find compensatory or co-regulation of protein
expression levels. In a novel experiment, we alter the distribution of
extracellular signaling, and observe adaptation based on optimizing signal
transmission. We also discuss the relationship between signaling with and
without transients. Signaling by transients may involve maximization of signal
transmission efficiency for the peak response, but a minimization in
steady-state responses. With an appropriate objective function, this can also
be achieved by concentration adjustment. Self-organizing systems may be
predictive of unwanted drug interference effects, since they aim to mimic
complex cellular adaptation in a unified way.Comment: updated version, 13 pages, 4 figures, 3 Tables, supplemental tabl
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms
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