An island based hybrid evolutionary algorithm for optimization

This is a post-print version of the article - Copyright @ 2008 Springer-VerlagEvolutionary computation has become an important problem solving methodology among the set of search and optimization techniques. Recently, more and more different evolutionary techniques have been developed, especially hybrid evolutionary algorithms. This paper proposes an island based hybrid evolutionary algorithm (IHEA) for optimization, which is based on Particle swarm optimization (PSO), Fast Evolutionary Programming (FEP), and Estimation of Distribution Algorithm (EDA). Within IHEA, an island model is designed to cooperatively search for the global optima in search space. By combining the strengths of the three component algorithms, IHEA greatly improves the optimization performance of the three basic algorithms. Experimental results demonstrate that IHEA outperforms all the three component algorithms on the test problems.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1

Application of quantum-inspired generative models to small molecular datasets

Quantum and quantum-inspired machine learning has emerged as a promising and challenging research field due to the increased popularity of quantum computing, especially with near-term devices. Theoretical contributions point toward generative modeling as a promising direction to realize the first examples of real-world quantum advantages from these technologies. A few empirical studies also demonstrate such potential, especially when considering quantum-inspired models based on tensor networks. In this work, we apply tensor-network-based generative models to the problem of molecular discovery. In our approach, we utilize two small molecular datasets: a subset of $4989$ molecules from the QM9 dataset and a small in-house dataset of $516$ validated antioxidants from TotalEnergies. We compare several tensor network models against a generative adversarial network using different sample-based metrics, which reflect their learning performances on each task, and multiobjective performances using $3$ relevant molecular metrics per task. We also combined the output of the models and demonstrate empirically that such a combination can be beneficial, advocating for the unification of classical and quantum(-inspired) generative learning.Comment: First versio

Antizyme inhibitor 2 (AZIN2) associates with better prognosis of head and neck minor salivary gland adenoid cystic carcinoma

cited By 0The key regulator of the polyamine biosynthetic pathway is ornithine decarboxylase (ODC). ODC is activated by antizyme inhibitor 1 (AZIN1) and 2 (AZIN2). AZIN1 and recently AZIN2 have been related to cancer; however, their functions in adenoid cystic carcinoma (ACC) have not been studied. We performed immunohistochemical study on minor salivary and mucous gland ACC tissue samples of patients treated at the Helsinki University Hospital (Helsinki, Finland) during 1974-2012. We scored AZIN1 and 2 immunoexpression in 42 and 45 tumor tissue samples, respectively, and correlated them with clinicopathological factors and survival. Enhanced AZIN2 expression was associated with better survival. In addition, both AZINs were seen more commonly in cribriform and tubular than in solid growth patterns. AZIN1 expression did not correlate with the studied clinicopathological factors. It seems that AZIN2 expression is higher in cancer tissue with secretory functions. In ACC tissue, high AZIN2 expression could be related to well-differentiated histological type which still has a functioning vesicle transportation system. Thus, AZIN2 could be a prognostic factor for better survival of ACC patients.Peer reviewe

Coherence features of the spin-aligned neutron-proton pair coupling scheme

The seniority scheme has been shown to be extremely useful for the classification of nuclear states in semi-magic nuclei. The neutron-proton ($np$) correlation breaks the seniority symmetry in a major way. As a result, the corresponding wave function is a mixture of many components with different seniority quantum numbers. In this contribution we show that the $np$ interaction may favor a new kind of coupling in $N=Z$ nuclei, i.e., the so-called isoscalar spin-aligned $np$ pair mode. Shell model calculations reveal that the ground and low-lying yrast states of the $N = Z$ nuclei $^{92}$Pd and $^{96}$Cd may mainly be built upon such spin-aligned $np$ pairs each carrying the maximum angular momentum $J = 9$ allowed by the shell $0g_{9/2}$ which is dominant in this nuclear region.Comment: 5 pages, 4 figues, Proceedings of the Nordic Conference on Nuclear Physics 2011. To appear in Physica Scripta

Unsupervised strategies for identifying optimal parameters in Quantum Approximate Optimization Algorithm

As combinatorial optimization is one of the main quantum computing applications, many methods based on parameterized quantum circuits are being developed. In general, a set of parameters are being tweaked to optimize a cost function out of the quantum circuit output. One of these algorithms, the Quantum Approximate Optimization Algorithm stands out as a promising approach to tackling combinatorial problems. However, finding the appropriate parameters is a difficult task. Although QAOA exhibits concentration properties, they can depend on instances characteristics that may not be easy to identify, but may nonetheless offer useful information to find good parameters. In this work, we study unsupervised Machine Learning approaches for setting these parameters without optimization. We perform clustering with the angle values but also instances encodings (using instance features or the output of a variational graph autoencoder), and compare different approaches. These angle-finding strategies can be used to reduce calls to quantum circuits when leveraging QAOA as a subroutine. We showcase them within Recursive-QAOA up to depth 3 where the number of QAOA parameters used per iteration is limited to 3, achieving a median approximation ratio of 0.94 for MaxCut over 200 Erdős-Rényi graphs. We obtain similar performances to the case where we extensively optimize the angles, hence saving numerous circuit calls.Algorithms and the Foundations of Software technolog

Lipoprotein(a) in Alzheimer, Atherosclerotic, Cerebrovascular, Thrombotic, and Valvular Disease: Mendelian Randomization Investigation.

Lipoprotein(a) (Lp[a]) is a circulating lipoprotein with proatherogenic, proinflammatory, and possibly prothrombotic properties. Circulating Lp(a) levels are largely genetically determined, in particular, by the LPA gene. As such, genetic variants at the LPA locus can serve as instrumental variables for investigating the clinical effects of circulating Lp(a) levels. Mendelian randomization (MR) studies have shown that elevated Lp(a) levels are associated with a higher risk of coronary artery disease1–3 and aortic valve stenosis.2–4 Evidence on the causal role of elevated Lp(a) levels for other atherosclerotic and specific valvular diseases is limited, although there are MR data supporting a positive association between genetically predicted Lp(a) levels and peripheral artery disease.2,3 Whether Lp(a) is causally related to thrombotic disease and cerebrovascular disease remains unclear.2,3,5 In this study, we used the UK Biobank cohort to perform an MR investigation into the causal effects of circulating Lp(a) levels on atherosclerotic, cerebrovascular, thrombotic, and valvular diseases. Because a recent MR study provided evidence of an inverse association of Lp(a) levels with Alzheimer disease,5 we additionally explored whether genetically predicted Lp(a) levels are associated with Alzheimer disease and dementia.Dr Larsson receives support from the Swedish Heart-Lung Foundation (Hjärt-Lungfonden, grant number 20190247), the Swedish Research Council (Vetenskapsrådet, grant number 2019-00977), and the Swedish Research Council for Health, Working Life and Welfare (Forte, grant number 2018-00123). Dr Gill is funded by the Wellcome 4i Clinical PhD Program at Imperial College London. Dr Burgess is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (award number 204623/Z/16/Z). Drs Burgess and Butterworth report funding from Novartis relating to the investigation of lipoprotein(a). The funder had no influence on the content of the investigation or the decision to publish. This work was supported by core funding from the UK Medical Research Council (MR/L003120/1), the British Heart Foundation (RG/13/13/30194; RG/18/13/33946), the National Institute for Health Research [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust] and Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome

Benchmarking and analyzing iterative optimization heuristics with IOHprofiler

Algorithms and the Foundations of Software technolog

Properties of Nucleon Resonances by means of a Genetic Algorithm

We present an optimization scheme that employs a Genetic Algorithm (GA) to determine the properties of low-lying nucleon excitations within a realistic photo-pion production model based upon an effective Lagrangian. We show that with this modern optimization technique it is possible to reliably assess the parameters of the resonances and the associated error bars as well as to identify weaknesses in the models. To illustrate the problems the optimization process may encounter, we provide results obtained for the nucleon resonances $\Delta$(1230) and $\Delta$(1700). The former can be easily isolated and thus has been studied in depth, while the latter is not as well known experimentally.Comment: 12 pages, 10 figures, 3 tables. Minor correction

Hyperparameter importance of quantum neural networks across small datasets

As restricted quantum computers are slowly becoming a reality, the search for meaningful first applications intensifies. In this domain, one of the more investigated approaches is the use of a special type of quantum circuit – a so-called quantum neural network – to serve as a basis for a machine learning model. Roughly speaking, as the name suggests, a quantum neural network can play a similar role to a neural network. However, specifically for applications in machine learning contexts, very little is known about suitable circuit architectures, or model hyperparameters one should use to achieve good learning performance. In this work, we apply the functional ANOVA framework to quantum neural networks to analyze which of the hyperparameters were most influential for their predictive performance. We analyze one of the most typically used quantum neural network architectures. We then apply this to 7 open-source datasets from the OpenML-CC18 classification benchmark whose number of features is small enough to fit on quantum hardware with less than 20 qubits. Three main levels of importance were detected from the ranking of hyperparameters obtained with functional ANOVA. Our experiment both confirmed expected patterns and revealed new insights. For instance, setting well the learning rate is deemed the most critical hyperparameter in terms of marginal contribution on all datasets, whereas the particular choice of entangling gates used is considered the least important except on one dataset. This work introduces new methodologies to study quantum machine learning models and provides new insights toward quantum model selection.Algorithms and the Foundations of Software technolog

Adaptive walks on time-dependent fitness landscapes

The idea of adaptive walks on fitness landscapes as a means of studying evolutionary processes on large time scales is extended to fitness landscapes that are slowly changing over time. The influence of ruggedness and of the amount of static fitness contributions are investigated for model landscapes derived from Kauffman's $NK$ landscapes. Depending on the amount of static fitness contributions in the landscape, the evolutionary dynamics can be divided into a percolating and a non-percolating phase. In the percolating phase, the walker performs a random walk over the regions of the landscape with high fitness.Comment: 7 pages, 6 eps-figures, RevTeX, submitted to Phys. Rev.