Decomposition for Large-scale Optimization Problems with Overlapping Components

In this paper we use a divide-and-conquer approach to tackle large-scale optimization problems with overlapping components. Decomposition for an overlapping problem is challenging as its components depend on one another. The existing decomposition methods typically assign all the linked decision variables into one group, thus cannot reduce the original problem size. To address this issue we modify the Recursive Differential Grouping (RDG) method to decompose overlapping problems, by breaking the linkage at variables shared by multiple components. To evaluate the efficacy of our method, we extend two existing overlapping benchmark problems considering various level of overlap. Experimental results show that our method can greatly improve the search ability of an optimization algorithm via divide-and-conquer, and outperforms RDG, random decomposition as well as other state-of-the-art methods. We further evaluate our method using the CEC'2013 benchmark problems and show that our method is very competitive when equipped with a component optimizer

Complete spatial safety for C and C++ using CHERI capabilities

Lack of memory safety in commonly used systems-level languages such as C and C++ results in a constant stream of new exploitable software vulnerabilities and exploit techniques. Many exploit mitigations have been proposed and deployed over the years, yet none address the root issue: lack of memory safety. Most C and C++ implementations assume a memory model based on a linear array of bytes rather than an object-centric view. Whilst more efficient on contemporary CPU architectures, linear addresses cannot encode the target object, thus permitting memory errors such as spatial safety violations (ignoring the bounds of an object). One promising mechanism to provide memory safety is CHERI (Capability Hardware Enhanced RISC Instructions), which extends existing processor architectures with capabilities that provide hardware-enforced checks for all accesses and can be used to prevent spatial memory violations. This dissertation prototypes and evaluates a pure-capability programming model (using CHERI capabilities for all pointers) to provide complete spatial memory protection for traditionally unsafe languages. As the first step towards memory safety, all language-visible pointers can be implemented as capabilities. I analyse the programmer-visible impact of this change and refine the pure-capability programming model to provide strong source-level compatibility with existing code. Second, to provide robust spatial safety, language-invisible pointers (mostly arising from program linkage) such as those used for functions calls and global variable accesses must also be protected. In doing so, I highlight trade-offs between performance and privilege minimization for implicit and programmer-visible pointers. Finally, I present CheriSH, a novel and highly compatible technique that protects against buffer overflows between fields of the same object, hereby ensuring that the CHERI spatial memory protection is complete. I find that the byte-granular spatial safety provided by CHERI pure-capability code is not only stronger than most other approaches, but also incurs almost negligible performance overheads in common cases (0.1% geometric mean) and a worst-case overhead of only 23.3% compared to the insecure MIPS baseline. Moreover, I show that the pure-capability programming model provides near-complete source-level compatibility with existing programs. I evaluate this based on porting large widely used open-source applications such as PostgreSQL and WebKit with only minimal changes: fewer than 0.1% of source lines. I conclude that pure-capability CHERI C/C++ is an eminently viable programming environment offering strong memory protection, good source-level compatibility and low performance overheads

GAMBIT: A parameterless model-based evolutionary algorithm for mixed-integer problems

Learning and exploiting problem structure is one of the key challenges in optimization. This is especially important for black-box optimization (BBO) where prior structural knowledge of a problem is not available. Existing model-based Evolutionary Algorithms (EAs) are very efficient at learning structure in both the discrete, and in the continuous domain. In this article, discrete and continuous model-building mechanisms are integrated for the Mixed-Integer (MI) domain, comprising discrete and continuous variables. We revisit a recently introduced model-based evolutionary algorithm for the MI domain, the Genetic Algorithm for Model-Based mixed-Integer opTimization (GAMBIT). We extend GAMBIT with a parameterless scheme that allows for practical use of the algorithm without the need to explicitly specify any parameters. We furthermore contrast GAMBIT with other model-based alternatives. The ultimate goal of processing mixed dependences explicitly in GAMBIT is also addressed by introducing a new mechanism for the explicit exploitation of mixed dependences. We find that processing mixed dependences with this novel mechanism allows for more efficient optimization. We further contrast the parameterless GAMBIT with Mixed-Integer Evolution Strategies (MIES) and other state-of-the-art MI optimization algorithms from the General Algebraic Modeling System (GAMS) commercial algorithm suite on problems with and without constraints, and show that GAMBIT is capable of solving problems where variable dependences prevent many algorithms from successfully optimizing them

Gene prioritization and clustering by multi-view text mining

<p>Abstract</p> <p>Background</p> <p>Text mining has become a useful tool for biologists trying to understand the genetics of diseases. In particular, it can help identify the most interesting candidate genes for a disease for further experimental analysis. Many text mining approaches have been introduced, but the effect of disease-gene identification varies in different text mining models. Thus, the idea of incorporating more text mining models may be beneficial to obtain more refined and accurate knowledge. However, how to effectively combine these models still remains a challenging question in machine learning. In particular, it is a non-trivial issue to guarantee that the integrated model performs better than the best individual model.</p> <p>Results</p> <p>We present a multi-view approach to retrieve biomedical knowledge using different controlled vocabularies. These controlled vocabularies are selected on the basis of nine well-known bio-ontologies and are applied to index the vast amounts of gene-based free-text information available in the MEDLINE repository. The text mining result specified by a vocabulary is considered as a view and the obtained multiple views are integrated by multi-source learning algorithms. We investigate the effect of integration in two fundamental computational disease gene identification tasks: gene prioritization and gene clustering. The performance of the proposed approach is systematically evaluated and compared on real benchmark data sets. In both tasks, the multi-view approach demonstrates significantly better performance than other comparing methods.</p> <p>Conclusions</p> <p>In practical research, the relevance of specific vocabulary pertaining to the task is usually unknown. In such case, multi-view text mining is a superior and promising strategy for text-based disease gene identification.</p