The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems

This paper investigates the impact of geometric semantic crossover operators in a wide range of symbolic regression problems. First, it analyses the impact of using Manhattan and Euclidean distance geometric semantic crossovers in the learning process. Then, it proposes two strategies to numerically optimize the crossover mask based on mathematical properties of these operators, instead of simply generating them randomly. An experimental analysis comparing geometric semantic crossovers using Euclidean and Manhattan distances and the proposed strategies is performed in a test bed of twenty datasets. The results show that the use of different distance functions in the semantic geometric crossover has little impact on the test error, and that our optimized crossover masks yield slightly better results. For SGP practitioners, we suggest the use of the semantic crossover based on the Euclidean distance, as it achieved similar results to those obtained by more complex operators

Revisiting the Sequential Symbolic Regression Genetic Programming

Sequential Symbolic Regression (SSR) is a technique that recursively induces functions over the error of the current solution, concatenating them in an attempt to reduce the error of the resulting model. As proof of concept, the method was previously evaluated in one-dimensional problems and compared with canonical Genetic Programming (GP) and Geometric Semantic Genetic Programming (GSGP). In this paper we revisit SSR exploring the method behaviour in higher dimensional, larger and more heterogeneous datasets. We discuss the difficulties arising from the application of the method to more complex problems, e.g., overfitting, along with suggestions to overcome them. An experimental analysis was conducted comparing SSR to GP and GSGP, showing SSR solutions are smaller than those generated by the GSGP with similar performance and more accurate than those generated by the canonical GP

Reducing Dimensionality to Improve Search in Semantic Genetic Programming

Genetic programming approaches are moving from analysing the syntax of individual solutions to look into their semantics. One of the common definitions of the semantic space in the context of symbolic regression is a n-dimensional space, where n corresponds to the number of training examples. In problems where this number is high, the search process can became harder as the number of dimensions increase. Geometric semantic genetic programming (GSGP) explores the semantic space by performing geometric semantic operations—the fitness landscape seen by GSGP is guaranteed to be conic by construction. Intuitively, a lower number of dimensions can make search more feasible in this scenario, decreasing the chances of data overfitting and reducing the number of evaluations required to find a suitable solution. This paper proposes two approaches for dimensionality reduction in GSGP: (i) to apply current instance selection methods as a pre-process step before training points are given to GSGP; (ii) to incorporate instance selection to the evolution of GSGP. Experiments in 15 datasets show that GSGP performance is improved by using instance reduction during the evolution

A Generic Framework for Building Dispersion Operators in the Semantic Space

This chapter proposes a generic framework to build geometric dispersion (GD) operators for Geometric Semantic Genetic Programming in the context of symbolic regression, followed by two concrete instantiations of the framework: a multiplicative geometric dispersion operator and an additive geometric dispersion operator. These operators move individuals in the semantic space in order to balance the population around the target output in each dimension, with the objective of expanding the convex hull defined by the population to include the desired output vector. An experimental analysis was conducted in a testbed composed of sixteen datasets showing that dispersion operators can improve GSGP search and that the multiplicative version of the operator is overall better than the additive version

Tremor in Patients with Inflammatory Bowel Disease

The spectrum of neurological manifestations of Crohn’s disease (CD) and ulcerative colitis (UC) is diverse and not fully understood. In addition to peripheral neuropathy, mild autonomic impairment has been reported. These problems themselves could also result in secondary neurological consequences, such as the exacerbation of enhanced physiological tremor. Indeed, some patients in our cohort complained of tremor. Hence, we hypothesized that enhanced physiological tremor would be more severe in patients with inflammatory bowel disease (IBD) than controls

Intravenous glutamine decreases lung and distal organ injury in an experimental model of abdominal sepsis

Introduction The protective effect of glutamine, as a pharmacological agent against lung injury, has been reported in experimental sepsis; however, its efficacy at improving oxygenation and lung mechanics, attenuating diaphragm and distal organ injury has to be better elucidated. In the present study, we tested the hypothesis that a single early intravenous dose of glutamine was associated not only with the improvement of lung morpho-function, but also the reduction of the inflammatory process and epithelial cell apoptosis in kidney, liver, and intestine villi. Methods Seventy-two Wistar rats were randomly assigned into four groups. Sepsis was induced by cecal ligation and puncture surgery (CLP), while a sham operated group was used as control (C). One hour after surgery, C and CLP groups were further randomized into subgroups receiving intravenous saline (1 ml, SAL) or glutamine (0.75 g/kg, Gln). At 48 hours, animals were anesthetized, and the following parameters were measured: arterial oxygenation, pulmonary mechanics, and diaphragm, lung, kidney, liver, and small intestine villi histology. At 18 and 48 hours, Cytokine-Induced Neutrophil Chemoattractant (CINC)-1, interleukin (IL)-6 and 10 were quantified in bronchoalveolar and peritoneal lavage fluids (BALF and PLF, respectively). Results CLP induced: a) deterioration of lung mechanics and gas exchange; b) ultrastructural changes of lung parenchyma and diaphragm; and c) lung and distal organ epithelial cell apoptosis. Glutamine improved survival rate, oxygenation and lung mechanics, minimized pulmonary and diaphragmatic changes, attenuating lung and distal organ epithelial cell apoptosis. Glutamine increased IL-10 in peritoneal lavage fluid at 18 hours and bronchoalveolar lavage fluid at 48 hours, but decreased CINC-1 and IL-6 in BALF and PLF only at 18 hours. Conclusions In an experimental model of abdominal sepsis, a single intravenous dose of glutamine administered after sepsis induction may modulate the inflammatory process reducing not only the risk of lung injury, but also distal organ impairment. These results suggest that intravenous glutamine may be a potentially beneficial therapy for abdominal sepsis.Centres of Excellence Program (PRONEX-FAPERJ)Brazilian Council for Scientific and Technological Development (CNPq)Carlos Chagas FilhoRio de Janeiro State Research Supporting Foundation (FAPERJ)Sao Paulo State Research Supporting Foundation (FAPESP

Exposição a pesticidas e genótipo heterozigoto de GSTP1-Alw26I associam-se à doença de Parkinson

Objective This study aimed to analyze the frequency of GSTP1-Alw26I polymorphism and to estimate its association with toxic substances in Parkinson's disease (PD). Methods A study group with 154 patients - subdivided into familial and sporadic PD groups - and 158 elderly individuals without the disease (control group) were evaluated. GSTP1-Alw26I polymorphism was analyzed by polymerase chain reaction/restriction fragment length polymorphism (PCR-RFLP). Results Patients were significantly more exposed to pesticides compared with the control group (p=0.0004), and the heterozygote genotype associated to exposure to pesticides also prevailed in patients (p=0.0001). Wild homozygote genotype was related to tobacco use (p=0.043) and alcoholism (p=0.033) in familial PD patients. Conclusion Exposure to pesticides is associated to PD, whose effect can be enhanced when combined with the heterozygote genotype of GSTP1-Alw26I. Also, large genetic and environmental studies considering tobacco use, alcoholism, GSTP1 and PD are necessary to confirm our findings.Objetivo Analisar a frequência do polimorfismo GSTP1-Alw26I, assim como estimar sua associação com substâncias tóxicas na doença de Parkinson (DP). Métodos A casuística avaliada foi composta por um grupo de estudo, com 154 pacientes, subdivididos em DP familial e esporádica, e outro com 158 idosos sem a doença (grupo controle). O polimorfismo GSTP1-Alw26I foi analisado por reação em cadeia da polimerase/polimorfismo de comprimento do fragmento de restrição (PCR/RFLP). Resultados Os pacientes foram significativamente mais expostos a pesticidas, comparados com o grupo controle (p=0,0004), e o genótipo heterozigoto associado a exposição a pesticidas também prevaleceu nos pacientes (p=0,0001). O genótipo homozigoto selvagem apresentou relação com tabagismo (p=0,043) e etilismo (p=0,033) em pacientes com DP familial. Desse modo, a exposição a pesticidas está associada à DP, cujo efeito pode ser potencializado quando combinado ao genótipo heterozigoto de GSTP1-Alw26I. Estudos genético-ambientais envolvendo tabagismo, etilismo, GSTP1 e DP devem ser realizados em casuísticas numerosas, confirmando essa associação.Sao Jose do Rio Preto Medical School Department of NeuroscienceFAMERPFederal University of São PauloHospital de BaseUNIFESPSciEL

An ant colony-based semi-supervised approach for learning classification rules

Semi-supervised learning methods create models from a few labeled instances and a great number of unlabeled instances. They appear as a good option in scenarios where there is a lot of unlabeled data and the process of labeling instances is expensive, such as those where most Web applications stand. This paper proposes a semi-supervised self-training algorithm called Ant-Labeler. Self-training algorithms take advantage of supervised learning algorithms to iteratively learn a model from the labeled instances and then use this model to classify unlabeled instances. The instances that receive labels with high confidence are moved from the unlabeled to the labeled set, and this process is repeated until a stopping criteria is met, such as labeling all unlabeled instances. Ant-Labeler uses an ACO algorithm as the supervised learning method in the self-training procedure to generate interpretable rule-based models—used as an ensemble to ensure accurate predictions. The pheromone matrix is reused across different executions of the ACO algorithm to avoid rebuilding the models from scratch every time the labeled set is updated. Results showed that the proposed algorithm obtains better predictive accuracy than three state-of-the-art algorithms in roughly half of the datasets on which it was tested, and the smaller the number of labeled instances, the better the Ant-Labeler performance

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