Evolutionary Approaches to Optimization Problems in Chimera Topologies

Chimera graphs define the topology of one of the first commercially available quantum computers. A variety of optimization problems have been mapped to this topology to evaluate the behavior of quantum enhanced optimization heuristics in relation to other optimizers, being able to efficiently solve problems classically to use them as benchmarks for quantum machines. In this paper we investigate for the first time the use of Evolutionary Algorithms (EAs) on Ising spin glass instances defined on the Chimera topology. Three genetic algorithms (GAs) and three estimation of distribution algorithms (EDAs) are evaluated over $1000$ hard instances of the Ising spin glass constructed from Sidon sets. We focus on determining whether the information about the topology of the graph can be used to improve the results of EAs and on identifying the characteristics of the Ising instances that influence the success rate of GAs and EDAs.Comment: 8 pages, 5 figures, 3 table

Digging into acceptor splice site prediction : an iterative feature selection approach

Feature selection techniques are often used to reduce data dimensionality, increase classification performance, and gain insight into the processes that generated the data. In this paper, we describe an iterative procedure of feature selection and feature construction steps, improving the classification of acceptor splice sites, an important subtask of gene prediction. We show that acceptor prediction can benefit from feature selection, and describe how feature selection techniques can be used to gain new insights in the classification of acceptor sites. This is illustrated by the identification of a new, biologically motivated feature: the AG-scanning feature. The results described in this paper contribute both to the domain of gene prediction, and to research in feature selection techniques, describing a new wrapper based feature weighting method that aids in knowledge discovery when dealing with complex datasets

Population Sizing for the Redundant Trivial Voting Mapping

Mapping-Proble

Comparison of Two Core Biopsy Techniques Before and After Laparoscopic Cryoablation of Small Renal Cortical Neoplasms

A pre-ablation standard biopsy technique resulted in the most accurate pathologic diagnosis for patients undergoing cryoablation for renal cortical neoplasms

Thorax support vest to prevent sternal wound infections in cardiac surgery patients—a systematic review and meta-analysis

OBJECTIVES: Midline sternotomy is the main surgical access for cardiac surgeries. The most prominent complication of sternotomy is sternal wound infection (SWI). The use of a thorax support vest (TSV) that limits thorax movement and ensures sternal stability has been suggested to prevent postoperative SWI. METHODS: We performed a meta-analysis to evaluate differences in clinical outcomes with and without the use of TSV after cardiac surgery in randomized trials. The primary outcome was deep SWI (DSWI). Secondary outcomes were superficial SWI, sternal wound dehiscence, and hospital length of stay (LOS). A trial sequential analysis was performed. Fixed (F) and random effects (R) models were calculated. RESULTS: A total of 4 studies (3820 patients) were included. Patients who wore the TSV had lower incidence of DSWI [odds ratio (OR) = F: 0.24, 95% confidence interval (CI), 0.13–0.43, P < 0.01; R: 0.24, 0.04–1.59, P = 0.08], sternal wound dehiscence (OR = F: 0.08, 95% CI, 0.02–0.27, P < 0.01; R: 0.10, 0.00–2.20, P = 0.08) and shorter hospital LOS (standardized mean difference = F: −0.30, −0.37 to −0.24, P < 0.01; R: −0.63, −1.29 to 0.02, P = 0.15). There was no difference regarding the incidence of superficial SWI (OR = F: 0.71, 95% CI, 0.34–1.47, P = 0.35; R: 0.64, 0.10, 4.26, P = 0.42). The trial sequential analysis, however, showed that the observed decrease in DSWI in the TSV arm cannot be considered conclusive based on the existing evidence. CONCLUSIONS: This meta-analysis suggests that the use of a TSV after cardiac surgery could potentially be associated with a reduction in sternal wound complications. However, despite the significant treatment effect in the available studies, the evidence is not solid enough to provide strong practice recommendations

Runtime Analysis of Probabilistic Crowding and Restricted Tournament Selection for Bimodal Optimisation

Many real optimisation problems lead to multimodal domains and so require the identifi- cation of multiple optima. Niching methods have been developed to maintain the population diversity, to investigate many peaks in parallel and to reduce the effect of genetic drift. Using rigorous runtime analysis, we analyse for the first time two well known niching methods: probabilistic crowding and restricted tournament selection (RTS). We incorporate both methods into a (µ+1) EA on the bimodal function Twomax where the goal is to find two optima at opposite ends of the search space. In probabilistic crowding, the offspring compete with their parents and the survivor is chosen proportionally to its fitness. On Twomax probabilistic crowding fails to find any reasonable solution quality even in exponential time. In RTS the offspring compete against the closest individual amongst w (window size) individuals. We prove that RTS fails if w is too small, leading to exponential times with high probability. However, if w is chosen large enough, it finds both optima for Twomax in time O(µn log n) with high probability. Our theoretical results are accompanied by experimental studies that match the theoretical results and also shed light on parameters not covered by the theoretical results

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

A review of estimation of distribution algorithms in bioinformatics

Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain