Advanced p-Metric Based Many-Objective Evolutionary Algorithm

Evolutionary many objective based optimization has been gaining a lot of attention from the evolutionary computation researchers and computational intelligence community. Many of the state-of-the-art multi-objective and many-objective optimization problems (MOPs, MaOPs) are inefficient in maintaining the convergence and diversity performances as the number of objectives increases in the modern-day real-world applications. This phenomenon is obvious indeed as Pareto-dominance based EAs employ non-dominated sorting which fails considerably in providing enough convergent pressure towards the Pareto front (PF). Researchers invested much more time and effort in addressing this issue by improving the scalability in MaOPs and they have come up with non-Pareto-dominance-based EAs such as decomposition-based, indicator-based and reference-based approaches. In addition to that, the algorithm has to account for the additional computational budget. This thesis proposes an advanced polar-metric (p-metric) based Many-objective EA (in short APMOEA) for tackling both MOPs and MaOPs. p-metric, a recently proposed performance based visualization metric, employs an array of uniformly, distributed direction vectors. In APMOEA, a two-phase selection scheme is employed which combines both non-dominated sorting and p-metric. Moreover, this thesis also proposes a modified P-metric methodology in order to adjust the direction vectors dynamically. In the experiments, we compare APMOEA with four state-of-the-art Many-objective EAs under, three performance indicators. According to the empirical results, APMOEA shows much improved performances on most of the test problems, involving both MOPs and MaOPs.Electrical Engineerin

Parametric t-Distributed Stochastic Exemplar-centered Embedding

Parametric embedding methods such as parametric t-SNE (pt-SNE) have been widely adopted for data visualization and out-of-sample data embedding without further computationally expensive optimization or approximation. However, the performance of pt-SNE is highly sensitive to the hyper-parameter batch size due to conflicting optimization goals, and often produces dramatically different embeddings with different choices of user-defined perplexities. To effectively solve these issues, we present parametric t-distributed stochastic exemplar-centered embedding methods. Our strategy learns embedding parameters by comparing given data only with precomputed exemplars, resulting in a cost function with linear computational and memory complexity, which is further reduced by noise contrastive samples. Moreover, we propose a shallow embedding network with high-order feature interactions for data visualization, which is much easier to tune but produces comparable performance in contrast to a deep neural network employed by pt-SNE. We empirically demonstrate, using several benchmark datasets, that our proposed methods significantly outperform pt-SNE in terms of robustness, visual effects, and quantitative evaluations.Comment: fixed typo

Learning Deep Similarity Metric for 3D MR-TRUS Registration

Purpose: The fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images for guiding targeted prostate biopsy has significantly improved the biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image registration. However, it is very challenging to obtain a robust automatic MR-TRUS registration due to the large appearance difference between the two imaging modalities. The work presented in this paper aims to tackle this problem by addressing two challenges: (i) the definition of a suitable similarity metric and (ii) the determination of a suitable optimization strategy. Methods: This work proposes the use of a deep convolutional neural network to learn a similarity metric for MR-TRUS registration. We also use a composite optimization strategy that explores the solution space in order to search for a suitable initialization for the second-order optimization of the learned metric. Further, a multi-pass approach is used in order to smooth the metric for optimization. Results: The learned similarity metric outperforms the classical mutual information and also the state-of-the-art MIND feature based methods. The results indicate that the overall registration framework has a large capture range. The proposed deep similarity metric based approach obtained a mean TRE of 3.86mm (with an initial TRE of 16mm) for this challenging problem. Conclusion: A similarity metric that is learned using a deep neural network can be used to assess the quality of any given image registration and can be used in conjunction with the aforementioned optimization framework to perform automatic registration that is robust to poor initialization.Comment: To appear on IJCAR