Bandit-based cooperative coevolution for tackling contribution imbalance in large-scale optimization problems

This paper addresses the issue of computational resource allocation within the context of cooperative coevolution. Cooperative coevolution typically works by breaking a problem down into smaller subproblems (or components) and coevolving them in a round-robin fashion, resulting in a uniform resource allocation among its components. Despite its success on a wide range of problems, cooperative coevolution struggles to perform efficiently when its components do not contribute equally to the overall objective value. This is of crucial importance on large-scale optimization problems where such difference are further magnified. To resolve this imbalance problem, we extend the standard cooperative coevolution to a new generic framework capable of learning the contribution of each component using multi-armed bandit techniques. The new framework allocates the computational resources to each component proportional to their contributions towards improving the overall objective value. This approach results in a more economical use of the limited computational resources. We study different aspects of the proposed framework in the light of extensive experiments. Our empirical results confirm that even a simple bandit-based credit assignment scheme can significantly improve the performance of cooperative coevolution on large-scale continuous problems, leading to competitive performance as compared to the state-of-the-art algorithms

SQG-Differential Evolution for difficult optimization problems under a tight function evaluation budget

In the context of industrial engineering, it is important to integrate efficient computational optimization methods in the product development process. Some of the most challenging simulation-based engineering design optimization problems are characterized by: a large number of design variables, the absence of analytical gradients, highly non-linear objectives and a limited function evaluation budget. Although a huge variety of different optimization algorithms is available, the development and selection of efficient algorithms for problems with these industrial relevant characteristics, remains a challenge. In this communication, a hybrid variant of Differential Evolution (DE) is introduced which combines aspects of Stochastic Quasi-Gradient (SQG) methods within the framework of DE, in order to improve optimization efficiency on problems with the previously mentioned characteristics. The performance of the resulting derivative-free algorithm is compared with other state-of-the-art DE variants on 25 commonly used benchmark functions, under tight function evaluation budget constraints of 1000 evaluations. The experimental results indicate that the new algorithm performs excellent on the 'difficult' (high dimensional, multi-modal, inseparable) test functions. The operations used in the proposed mutation scheme, are computationally inexpensive, and can be easily implemented in existing differential evolution variants or other population-based optimization algorithms by a few lines of program code as an non-invasive optional setting. Besides the applicability of the presented algorithm by itself, the described concepts can serve as a useful and interesting addition to the algorithmic operators in the frameworks of heuristics and evolutionary optimization and computing

Use of the q-Gaussian mutation in evolutionary algorithms

Copyright @ Springer-Verlag 2010.This paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the mutation distribution in evolutionary algorithms. The shape of the q-Gaussian mutation distribution is controlled by a real parameter q. In the proposed method, the real parameter q of the q-Gaussian mutation is encoded in the chromosome of individuals and hence is allowed to evolve during the evolutionary process. In order to test the new mutation operator, evolution strategy and evolutionary programming algorithms with self-adapted q-Gaussian mutation generated from anisotropic and isotropic distributions are presented. The theoretical analysis of the q-Gaussian mutation is also provided. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutations in the optimization of a set of test functions. Experimental results show the efficiency of the proposed method of self-adapting the mutation distribution in evolutionary algorithms.This work was supported in part by FAPESP and CNPq in Brazil and in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant EP/E060722/1 and Grant EP/E060722/2

Strategic emergency department design: An approach to capacity planning in healthcare provision in overcrowded emergency rooms

Healthcare professionals and the public have increasing concerns about the ability of emergency departments to meet current demands. Increased demand for emergency services, mainly caused by a growing number of minor and moderate injuries has reached crisis proportions, especially in the United Kingdom. Numerous efforts have been made to explore the complex causes because it is becoming more and more important to provide adequate healthcare within tight budgets. Optimisation of patient pathways in the emergency department is therefore an important factor

SMART: Unique splitting-while-merging framework for gene clustering

Copyright @ 2014 Fa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named “splitting merging awareness tactics” (SMART), which does not require any a priori knowledge of either the number of clusters or even the possible range of this number. Unlike existing self-splitting algorithms, which over-cluster the dataset to a large number of clusters and then merge some similar clusters, our framework has the ability to split and merge clusters automatically during the process and produces the the most reliable clustering results, by intrinsically integrating many clustering techniques and tasks. The SMART framework is implemented with two distinct clustering paradigms in two algorithms: competitive learning and finite mixture model. Nevertheless, within the proposed SMART framework, many other algorithms can be derived for different clustering paradigms. The minimum message length algorithm is integrated into the framework as the clustering selection criterion. The usefulness of the SMART framework and its algorithms is tested in demonstration datasets and simulated gene expression datasets. Moreover, two real microarray gene expression datasets are studied using this approach. Based on the performance of many metrics, all numerical results show that SMART is superior to compared existing self-splitting algorithms and traditional algorithms. Three main properties of the proposed SMART framework are summarized as: (1) needing no parameters dependent on the respective dataset or a priori knowledge about the datasets, (2) extendible to many different applications, (3) offering superior performance compared with counterpart algorithms.National Institute for Health Researc

Patellar tendon ossification after partial patellectomy: a case report

<p>Abstract</p> <p>Introduction</p> <p>Patellar tendon ossification is a rare pathology that may be seen as a complication after sleeve fractures of the tibial tuberosity, total patellectomy during arthroplasty, intramedullary nailing of tibial fractures, anterior cruciate ligament reconstruction with patellar tendon autograft and knee injury without fracture. However, its occurrence after partial patellectomy surgery has never been reported in the literature.</p> <p>Case presentation</p> <p>We present the case of a 35-year-old Turkish man with a comminuted inferior patellar pole fracture that was treated with partial patellectomy. During the follow-up period, his patellar tendon healed with ossification and then ruptured from the inferior attachment to the tibial tubercle. The ossification was excised and the tendon was subsequently repaired.</p> <p>Conclusion</p> <p>To the best of our knowledge, this is the first report of patellar tendon ossification occurring after partial patellectomy. Orthopaedic surgeons are thus cautioned to be conscious of this rare complication after partial patellectomy.</p

Perturbation with Intrabodies Reveals That Calpain Cleavage Is Required for Degradation of Huntingtin Exon 1

Background: Proteolytic processing of mutant huntingtin (mHtt), the protein that causes Huntington's disease (HD), is critical for mHtt toxicity and disease progression. mHtt contains several caspase and calpain cleavage sites that generate N-terminal fragments that are more toxic than full-length mHtt. Further processing is then required for the degradation of these fragments, which in turn, reduces toxicity. This unknown, secondary degradative process represents a promising therapeutic target for HD. Methodology/Principal Findings: We have used intrabodies, intracellularly expressed antibody fragments, to gain insight into the mechanism of mutant huntingtin exon 1 (mHDx-1) clearance. Happ1, an intrabody recognizing the proline-rich region of mHDx-1, reduces the level of soluble mHDx-1 by increasing clearance. While proteasome and macroautophagy inhibitors reduce turnover of mHDx-1, Happ1 is still able to reduce mHDx-1 under these conditions, indicating Happ1-accelerated mHDx-1 clearance does not rely on these processes. In contrast, a calpain inhibitor or an inhibitor of lysosomal pH block Happ1-mediated acceleration of mHDx-1 clearance. These results suggest that mHDx-1 is cleaved by calpain, likely followed by lysosomal degradation and this process regulates the turnover rate of mHDx-1. Sequence analysis identifies amino acid (AA) 15 as a potential calpain cleavage site. Calpain cleavage of recombinant mHDx-1 in vitro yields fragments of sizes corresponding to this prediction. Moreover, when the site is blocked by binding of another intrabody, V_L12.3, turnover of soluble mHDx-1 in living cells is blocked. Conclusions/Significance: These results indicate that calpain-mediated removal of the 15 N-terminal AAs is required for the degradation of mHDx-1, a finding that may have therapeutic implications

Accounting Problems Under the Excess Profits Tax

DNA vaccines based on subunits from pathogens have several advantages over other vaccine strategies. DNA vaccines can easily be modified, they show good safety profiles, are stable and inexpensive to produce, and the immune response can be focused to the antigen of interest. However, the immunogenicity of DNA vaccines which is generally quite low needs to be improved. Electroporation and co-delivery of genetically encoded immune adjuvants are two strategies aiming at increasing the efficacy of DNA vaccines. Here, we have examined whether targeting to antigen-presenting cells (APC) could increase the immune response to surface envelope glycoprotein (Env) gp120 from Human Immunodeficiency Virus type 1 (HIV- 1). To target APC, we utilized a homodimeric vaccine format denoted vaccibody, which enables covalent fusion of gp120 to molecules that can target APC. Two molecules were tested for their efficiency as targeting units: the antibody-derived single chain Fragment variable (scFv) specific for the major histocompatilibility complex (MHC) class II I-E molecules, and the CC chemokine ligand 3 (CCL3). The vaccines were delivered as DNA into muscle of mice with or without electroporation. Targeting of gp120 to MHC class II molecules induced antibodies that neutralized HIV-1 and that persisted for more than a year after one single immunization with electroporation. Targeting by CCL3 significantly increased the number of HIV-1 gp120-reactive CD8(+) T cells compared to non-targeted vaccines and gp120 delivered alone in the absence of electroporation. The data suggest that chemokines are promising molecular adjuvants because small amounts can attract immune cells and promote immune responses without advanced equipment such as electroporation.Funding Agencies|Research Council of Norway; Odd Fellow</p

Three-loop HTL QCD thermodynamics

The hard-thermal-loop perturbation theory (HTLpt) framework is used to calculate the thermodynamic functions of a quark-gluon plasma to three-loop order. This is the highest order accessible by finite temperature perturbation theory applied to a non-Abelian gauge theory before the high-temperature infrared catastrophe. All ultraviolet divergences are eliminated by renormalization of the vacuum, the HTL mass parameters, and the strong coupling constant. After choosing a prescription for the mass parameters, the three-loop results for the pressure and trace anomaly are found to be in very good agreement with recent lattice data down to $T \sim 2-3\,T_c$, which are temperatures accessible by current and forthcoming heavy-ion collision experiments.Comment: 27 pages, 11 figures; corresponds with published version in JHE