A hybrid cuckoo search and variable neighborhood descent for single and multiobjective scheduling problems

Cuckoo search (CS) is a relatively new meta-heuristic that has proven its strength in solving continuous optimization problems. This papers applies cuckoo search to the class of sequencing problems by hybridizing it with a variable neighborhood descent local search for enhancing the quality of the obtained solutions. The Lévy flight operator proposed in the original CS is modified to address the discrete nature of scheduling problems. Two well-known problems are used to demonstrate the effectiveness of the proposed hybrid CS approach. The first is the NP-hard single objective problem of minimizing the weighted total tardiness time (Formula presented.) and the second is the multiobjective problem of minimizing the flowtime ¯ and the maximum tardiness Tmaxfor single machine (Formula presented.). For the first problem, computational results show that the hybrid CS is able to find the optimal solutions for all benchmark test instances with 40, 50, and 100 jobs and for most instances with 150, 200, 250, and 300 jobs. For the second problem, the hybrid CS generated solutions on and very close to the exact Pareto fronts of test instances with 10, 20, 30, and 40 jobs. In general, the results reveal that the hybrid CS is an adequate and robust method for tackling single and multiobjective scheduling problems.

Application of cuckoo search for design optimization of heat exchangers

A wide variety of evolutionary optimization algorithms have been used by researcher for optimal design of shell and tube heat exchangers (STHX). The purpose of optimization is to minimize capital and operational costs subject to efficiency constraints. This paper comprehensively examines performance of genetic algorithm (GA) and cuckoo search (CS) for solving STHX design optimization. While GA has been widely adopted in the last decade for STHX optimal design, there is no report on application of CS method for this purpose. Simulation results in this paper demonstrate that CS greatly outperforms GA in terms of finding admissible and optimal configurations for STHX. It is also found that CS method not only has a lower computational requirement, but also generates the most consistent results.

Ipoll: Automatic polling using online search

For years, opinion polls rely on data collected through telephone or person-to-person surveys. The process is costly, inconvenient, and slow. Recently online search data has emerged as potential proxies for the survey data. However considerable human involvement is still needed for the selection of search indices, a task that requires knowledge of both the target issue and how search terms are used by the online community. The robustness of such manually selected search indices can be questionable. In this paper, we propose an automatic polling system through a novel application of machine learning. In this system, the needs for examining, comparing, and selecting search indices have been eliminated through automatic generation of candidate search indices and intelligent combination of the indices. The results include a publicly accessible web application that provides real-time, robust, and accurate measurements of public opinions on several subjects of general interest.

Tree-based iterated local search for Markov random fields with applications in image analysis

The maximum a posteriori assignment for general structure Markov random fields is computationally intractable. In this paper, we exploit tree-based methods to efficiently address this problem. Our novel method, named Tree-based Iterated Local Search (T-ILS), takes advantage of the tractability of tree-structures embedded within MRFs to derive strong local search in an ILS framework. The method efficiently explores exponentially large neighborhoods using a limited memory without any requirement on the cost functions. We evaluate the T-ILS on a simulated Ising model and two real-world vision problems: stereo matching and image denoising. Experimental results demonstrate that our methods are competitive against state-of-the-art rivals with significant computational gain.

An analysis of hatchling resting metabolism: in search of ecological correlates that explain deviations from allometric relations

From data in the literature, an allometric equation is compiled for hatchling resting metabolic rate and an attempt is made to explain residual variation in terms of hatchling type, yolk and water content, embryonic and postnatal growth rate, and environmental circumstances (latitudinal distribution). The body mass exponent for resting metabolism in hatchlings was 0.86 and, thus, substantially different from the values compiled for adult birds (0.67-0.75). Relatively high hatchling metabolic rates were found for birds exhibiting high embryonic and postnatal growth rates, as well as for those species that hatched at high latitudes. A functional explanation is postulated for the correlations between hatchling metabolism and these three variables.

Intelligent cuckoo search optimized traffic signal controllers for multi-intersection network

Traffic congestion in urban roads is one of the biggest challenges of 21 century. Despite a myriad of research work in the last two decades, optimization of traffic signals in network level is still an open research problem. This paper for the first time employs advanced cuckoo search optimization algorithm for optimally tuning parameters of intelligent controllers. Neural Network (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are two intelligent controllers implemented in this study. For the sake of comparison, we also implement Q-learning and fixed-time controllers as benchmarks. Comprehensive simulation scenarios are designed and executed for a traffic network composed of nine four-way intersections. Obtained results for a few scenarios demonstrate the optimality of trained intelligent controllers using the cuckoo search method. The average performance of NN, ANFIS, and Q-learning controllers against the fixed-time controller are 44%, 39%, and 35%, respectively.

Fuzzy system with tabu search learning for classification of motor imagery data

This paper introduces an approach to classify EEG signals using wavelet transform and a fuzzy standard additive model (FSAM) with tabu search learning mechanism. Wavelet coefficients are ranked based on statistics of the Wilcoxon test. The most informative coefficients are assembled to form a feature set that serves as inputs to the tabu-FSAM. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II are employed for the experiments. Classification performance is evaluated using accuracy, mutual information, Gini coefficient and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The proposed tabu-FSAM method considerably dominates the competitive classifiers, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II.

Who am I? 52 Tuesdays, intimacy and the search for an authentic life

Recent research shows that young people list media entertainment as one of the sources where they find information about what they really want to know about sex and what is not taught through the school curriculum – namely, relationships and eroticism. This paper addresses the potential role that may be played by small independent alternative feature films such as 52 Tuesdays in the sexual education of young people. While 52 Tuesdays’ purpose was never explicitly pedagogic, the subject matter – family relationships, sexual experimentation, sexual identity and agency, and transgender experience – situates it firmly within the concerns of contemporary young people.

Web search activity data accurately predict population chronic disease risk in the USA

BACKGROUND: The WHO framework for non-communicable disease (NCD) describes risks and outcomes comprising the majority of the global burden of disease. These factors are complex and interact at biological, behavioural, environmental and policy levels presenting challenges for population monitoring and intervention evaluation. This paper explores the utility of machine learning methods applied to population-level web search activity behaviour as a proxy for chronic disease risk factors. METHODS: Web activity output for each element of the WHO's Causes of NCD framework was used as a basis for identifying relevant web search activity from 2004 to 2013 for the USA. Multiple linear regression models with regularisation were used to generate predictive algorithms, mapping web search activity to Centers for Disease Control and Prevention (CDC) measured risk factor/disease prevalence. Predictions for subsequent target years not included in the model derivation were tested against CDC data from population surveys using Pearson correlation and Spearman's r. RESULTS: For 2011 and 2012, predicted prevalence was very strongly correlated with measured risk data ranging from fruits and vegetables consumed (r=0.81; 95% CI 0.68 to 0.89) to alcohol consumption (r=0.96; 95% CI 0.93 to 0.98). Mean difference between predicted and measured differences by State ranged from 0.03 to 2.16. Spearman's r for state-wise predicted versus measured prevalence varied from 0.82 to 0.93. CONCLUSIONS: The high predictive validity of web search activity for NCD risk has potential to provide real-time information on population risk during policy implementation and other population-level NCD prevention efforts.

Inflation tax in the lab: a theoretical and experimental study of competitive search equilibrium with inflation

In this paper we measure the effect of the inflation tax on economic activity and welfare within a controlled setting.To do so,we develop a model of price posting and monetary exchange with inflation and finite populations.The model,which provides a game–theoreticfoundation to Rocheteau and Wright(2005)'s competitive search monetary equilibrium, is used to derive theoretical propositions regarding the effects of inflation in thisenvironment, which we test with a laboratory experiment that closely implements the theoretical framework.We find that the inflation tax is harmful – with cash holdings, production and welfare all falling as inflation rises – and that its effect is relatively larger at low inflation rates than at higher rates.For instance,for inflation rates between 0%and5%, welfare in the two markets we consider (2[seller] 2[buyer] and 3 2) falls by roughly 1 percent for each percentage–point rise in inflation, compared with 0.4 percent over the range from 5% to 30%.Our findings lead us to conclude that the impact of the inflation tax should not be underestimated, even under low inflation.

A computational search for lipases that can preferentially hydrolyze long-chain omega-3 fatty acids from fish oil triacylglycerols

Consumption of long-chain omega-3 fatty acids is known to decrease the risk of major cardiovascular events. Lipases, a class of triacylglycerol hydrolases, have been extensively tested to concentrate omega-3 fatty acids from fish oils, under mild enzymatic conditions. However, no lipases with preference for omega-3 fatty acids selectivity have yet been discovered or developed. In this study we performed an exhaustive computational study of substrate-lipase interactions by docking, both covalent and non-covalent, for 38 lipases with a large number of structured triacylglycerols containing omega-3 fatty acids. We identified some lipases that have potential to preferentially hydrolyze omega-3 fatty acids from structured triacylglycerols. However omega-3 fatty acid preferences were found to be modest. Our study provides an explanation for absence of reports of lipases with omega-3 fatty acid hydrolyzing ability and suggests methods for developing these selective lipases.

Design of an optimal ANFIS traffic signal controller by using cuckoo search for an isolated intersection

An optimal design of Adaptive Neuro-Fuzzy Inference System (ANFIS) traffic signal controller is presented in this paper. The proposed controller aims to adjust a set of green times for traffic lights in a single intersection with the purpose of minimizing travel delay time and traffic congestion. The ANFIS controller is trained, to learned how to set green times for each traffic phase. This intelligent controller uses the Cuckoo Search (CS) algorithm to tune its parameters during the learning pried. Evaluating the performance of the proposed controller in comparison with the performance of a FLS controller (FLC) with predefined rules and membership functions, and also three fixed-Time controllers, illustrates the better performance of the optimal ANFIS controller against the other benchmark controllers.

Failing to see the woods for the trees: in search of critical creative research

Not all examples of creativity can be explained by the functionalist view that creativity is inherent in an artefact and comes about due to a “creative” individual’s efforts within a conducive environment. I build a case for creativity research which overturns assumptions inherent in functionalist research and conceptualises the phenomenon as a context specific, social construction. However, reviewing the limited critical research, I find that there is inconsistency in the theoretical models and the empirical work and ongoing privileging of the individual. It is unsurprising therefore, that, this research has been criticised for causing fragmentation within the field. I argue, to build knowledge, critical creativity research needs to adopt a systemic perspective, focus on social processes and consider collectives.