An analysis of the local optima storage capacity of Hopfield network based fitness function models

A Hopfield Neural Network (HNN) with a new weight update rule can be treated as a second order Estimation of Distribution Algorithm (EDA) or Fitness Function Model (FFM) for solving optimisation problems. The HNN models promising solutions and has a capacity for storing a certain number of local optima as low energy attractors. Solutions are generated by sampling the patterns stored in the attractors. The number of attractors a network can store (its capacity) has an impact on solution diversity and, consequently solution quality. This paper introduces two new HNN learning rules and presents the Hopfield EDA (HEDA), which learns weight values from samples of the fitness function. It investigates the attractor storage capacity of the HEDA and shows it to be equal to that known in the literature for a standard HNN. The relationship between HEDA capacity and linkage order is also investigated

Stochastic stability versus localization in chaotic dynamical systems

We prove stochastic stability of chaotic maps for a general class of Markov random perturbations (including singular ones) satisfying some kind of mixing conditions. One of the consequences of this statement is the proof of Ulam's conjecture about the approximation of the dynamics of a chaotic system by a finite state Markov chain. Conditions under which the localization phenomenon (i.e. stabilization of singular invariant measures) takes place are also considered. Our main tools are the so called bounded variation approach combined with the ergodic theorem of Ionescu-Tulcea and Marinescu, and a random walk argument that we apply to prove the absence of ``traps'' under the action of random perturbations.Comment: 27 pages, LaTe

The Univariate Marginal Distribution Algorithm Copes Well With Deception and Epistasis

In their recent work, Lehre and Nguyen (FOGA 2019) show that the univariate marginal distribution algorithm (UMDA) needs time exponential in the parent populations size to optimize the DeceptiveLeadingBlocks (DLB) problem. They conclude from this result that univariate EDAs have difficulties with deception and epistasis. In this work, we show that this negative finding is caused by an unfortunate choice of the parameters of the UMDA. When the population sizes are chosen large enough to prevent genetic drift, then the UMDA optimizes the DLB problem with high probability with at most $\lambda(\frac{n}{2} + 2 e \ln n)$ fitness evaluations. Since an offspring population size $\lambda$ of order $n \log n$ can prevent genetic drift, the UMDA can solve the DLB problem with $O(n^2 \log n)$ fitness evaluations. In contrast, for classic evolutionary algorithms no better run time guarantee than $O(n^3)$ is known (which we prove to be tight for the ${(1+1)}$ EA), so our result rather suggests that the UMDA can cope well with deception and epistatis. From a broader perspective, our result shows that the UMDA can cope better with local optima than evolutionary algorithms; such a result was previously known only for the compact genetic algorithm. Together with the lower bound of Lehre and Nguyen, our result for the first time rigorously proves that running EDAs in the regime with genetic drift can lead to drastic performance losses

Symmetry breaking perturbations and strange attractors

The asymmetrically forced, damped Duffing oscillator is introduced as a prototype model for analyzing the homoclinic tangle of symmetric dissipative systems with \textit{symmetry breaking} disturbances. Even a slight fixed asymmetry in the perturbation may cause a substantial change in the asymptotic behavior of the system, e.g. transitions from two sided to one sided strange attractors as the other parameters are varied. Moreover, slight asymmetries may cause substantial asymmetries in the relative size of the basins of attraction of the unforced nearly symmetric attracting regions. These changes seems to be associated with homoclinic bifurcations. Numerical evidence indicates that \textit{strange attractors} appear near curves corresponding to specific secondary homoclinic bifurcations. These curves are found using analytical perturbational tools

Health promotion profile of youth sports clubs in Finland: club officials' and coaches' perceptions

The purpose of this article is to examine the current health promotion orientation of youth sports clubs in Finland in view of the standards created previously for the health promoting sports club (HPSC). Ninety-seven youth sports clubs participated, and 273 sports club officials and 240 coaches answered the questionnaires. To describe clubs health promotion orientations, an HPSC index was created. The HPSC index was formulated on sub-indices by factor analysis. The sub-indices were: policy, ideology, practice and environment indexes. The results indicate that youth sports clubs are fairly health promoting in general. On average, the clubs fulfilled 12 standards for HPSC out of 22. Every fourth club was categorized as higher health promoting (≥ 15 fulfilled standards), and every third as lower health promoting (<11 fulfilled standards). The variation between clubs was wide. The clubs that had been recognized as exemplary and hence certified by the Young Finland Association were more likely to recognize health promotion than non-certified clubs (OR = 2.36, p = 0.016). The sports club officials were twice as likely to evaluate their clubs as higher health promoting than the coaches (OR = 2.04, p = 0.041). Under the sub-indices, ideologies were recognized best, others less. These findings indicate that minority of the youth sports clubs have realized health promotion comprehensively as a part of their activities. There is a lot of need for development, especially in the area of health promotion policies and practices. The instruments used proved valid and reliable and can therefore be recommended for international use

Advancing Model-Building for Many-Objective Optimization Estimation of Distribution Algorithms

Proceedings of: 3rd European Event on Bio-Inspired Algorithms for Continuous Parameter Optimisation (EvoNUM 2010) [associated to: EvoApplications 2010. European Conference on the Applications of Evolutionary Computation]. Istambul, Turkey, April 7-9, 2010In order to achieve a substantial improvement of MOEDAs regarding MOEAs it is necessary to adapt their model-building algorithms. Most current model-building schemes used so far off-the-shelf machine learning methods. These methods are mostly error-based learning algorithms. However, the model-building problem has specific requirements that those methods do not meet and even avoid. In this work we dissect this issue and propose a set of algorithms that can be used to bridge the gap of MOEDA application. A set of experiments are carried out in order to sustain our assertionsThis work was supported by projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM CONTEXTS S2009/TIC-1485 and DPS2008-07029-C02-0Publicad

Single Cell Transcriptomics Implicate Novel Monocyte and T Cell Immune Dysregulation in Sarcoidosis

Sarcoidosis is a systemic inflammatory disease characterized by infiltration of immune cells into granulomas. Previous gene expression studies using heterogeneous cell mixtures lack insight into cell-type-specific immune dysregulation. We performed the first single-cell RNA-sequencing study of sarcoidosis in peripheral immune cells in 48 patients and controls. Following unbiased clustering, differentially expressed genes were identified for 18 cell types and bioinformatically assessed for function and pathway enrichment. Our results reveal persistent activation of circulating classical monocytes with subsequent upregulation of trafficking molecules. Specifically, classical monocytes upregulated distinct markers of activation including adhesion molecules, pattern recognition receptors, and chemokine receptors, as well as enrichment of immunoregulatory pathways HMGB1, mTOR, and ephrin receptor signaling. Predictive modeling implicated TGFβ and mTOR signaling as drivers of persistent monocyte activation. Additionally, sarcoidosis T cell subsets displayed patterns of dysregulation. CD4 naïve T cells were enriched for markers of apoptosis and Th17/T(reg) differentiation, while effector T cells showed enrichment of anergy-related pathways. Differentially expressed genes in regulatory T cells suggested dysfunctional p53, cell death, and TNFR2 signaling. Using more sensitive technology and more precise units of measure, we identify cell-type specific, novel inflammatory and regulatory pathways. Based on our findings, we suggest a novel model involving four convergent arms of dysregulation: persistent hyperactivation of innate and adaptive immunity via classical monocytes and CD4 naïve T cells, regulatory T cell dysfunction, and effector T cell anergy. We further our understanding of the immunopathology of sarcoidosis and point to novel therapeutic targets

A review on probabilistic graphical models in evolutionary computation

Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms

Geometric Generalization of the Nelder-Mead Algorithm

The Nelder-Mead Algorithm (NMA) is an almost half-century old method for numerical optimization, and it is a close relative of Particle Swarm Optimization (PSO) and Differential Evolution (DE). Geometric Particle Swarm Optimization (GPSO) and Geometric Differential Evolution (GDE) are recently introduced formal generalization of traditional PSO and DE that apply naturally to both continuous and combinatorial spaces. In this paper, we generalize NMA to combinatorial search spaces by naturally extending its geometric interpretation to these spaces, analogously as what was done for the traditional PSO and DE algorithms, obtaining the Geometric Nelder-Mead Algorithm (GNMA)

The promotion and development of One Health at Swiss TPH and its greater potential

One Health, an integrated health concept, is now an integral part of health research and development. One Health overlaps with other integrated approaches to health such as EcoHealth or Planetary Health, which not only consider the patient or population groups but include them in the social-ecological context. One Health has gained the widest foothold politically, institutionally, and in operational implementation. Increasingly, One Health is becoming part of reporting under the International Health Legislation (IHR 2005). The Swiss Tropical and Public Health Institute (Swiss TPH) has played a part in these developments with one of the first mentions of One Health in the biomedical literature. Here, we summarise the history of ideas and processes that led to the development of One Health research and development at the Swiss TPH, clarify its theoretical and methodological foundations, and explore its larger societal potential as an integrated approach to thinking. The history of ideas and processes leading to the development of One Health research at the Swiss TPH were inspired by far-sighted and open ideas of the directors and heads of departments, without exerting too much influence. They followed the progressing work and supported it with further ideas. These in turn were taken up and further developed by a growing number of individual scientists. These ideas were related to other strands of knowledge from economics, molecular biology, anthropology, sociology, theology, and linguistics. We endeavour to relate Western biomedical forms of knowledge generation with other forms, such as Mayan medicine. One Health, in its present form, has been influenced by African mobile pastoralists' integrated thinking that have been taken up into Western epistemologies. The intercultural nature of global and regional One Health approaches will inevitably undergo further scrutiny of successful ways fostering inter-epistemic interaction. Now theoretically well grounded, the One Health approach of seeking benefits for all through better and more equitable cooperation can clearly be applied to engagement in solving major societal problems such as social inequality, animal protection and welfare, environmental protection, climate change mitigation, biodiversity conservation, and conflict transformation