Towards insightful algorithm selection for optimisation using meta-learning concepts

In this paper we propose a meta-learning inspired framework for analysing the performance of meta-heuristics for optimization problems, and developing insights into the relationships between search space characteristics of the problem instances and algorithm performance. Preliminary results based on several meta-heuristics for well-known instances of the Quadratic Assignment Problem are presented to illustrate the approach using both supervised and unsupervised learning methods.

Integrated modelling of cost-effective siting and operation of flow-control infrastructure for river ecosystem conservation

Wetland and floodplain ecosystems along many regulated rivers are highly stressed, primarily due to a lack of environmental flows of appropriate magnitude, frequency, duration, and timing to support ecological functions. In the absence of increased environmental flows, the ecological health of river ecosystems can be enhanced by the operation of existing and new flow-control infrastructure (weirs and regulators) to return more natural environmental flow regimes to specific areas. However, determining the optimal investment and operation strategies over time is a complex task due to several factors including the multiple environmental values attached to wetlands, spatial and temporal heterogeneity and dependencies, nonlinearity, and time-dependent decisions. This makes for a very large number of decision variables over a long planning horizon. The focus of this paper is the development of a nonlinear integer programming model that accommodates these complexities. The mathematical objective aims to return the natural flow regime of key components of river ecosystems in terms of flood timing, flood duration, and interflood period. We applied a 2-stage recursive heuristic using tabu search to solve the model and tested it on the entire South Australian River Murray floodplain. We conclude that modern meta-heuristics can be used to solve the very complex nonlinear problems with spatial and temporal dependencies typical of environmental flow allocation in regulated river ecosystems. The model has been used to inform the investment in, and operation of, flow-control infrastructure in the South Australian River Murray.

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.

Heterogeneous cooperative co-evolution memetic differential evolution algorithms for big data optimisation problems

Evolutionary algorithms (EAs) have recently been suggested as candidate for solving big data optimisation problems that involve very large number of variables and need to be analysed in a short period of time. However, EAs face scalability issue when dealing with big data problems. Moreover, the performance of EAs critically hinges on the utilised parameter values and operator types, thus it is impossible to design a single EA that can outperform all other on every problem instances. To address these challenges, we propose a heterogeneous framework that integrates a cooperative co-evolution method with various types of memetic algorithms. We use the cooperative co-evolution method to split the big problem into sub-problems in order to increase the efficiency of the solving process. The subproblems are then solved using various heterogeneous memetic algorithms. The proposed heterogeneous framework adaptively assigns, for each solution, different operators, parameter values and local search algorithm to efficiently explore and exploit the search space of the given problem instance. The performance of the proposed algorithm is assessed using the Big Data 2015 competition benchmark problems that contain data with and without noise. Experimental results demonstrate that the proposed algorithm, with the cooperative co-evolution method, performs better than without cooperative co-evolution method. Furthermore, it obtained very competitive results for all tested instances, if not better, when compared to other algorithms using a lower computational times.

Unions and productivity growth: a meta-analytic review

The impact of unions on productivity growth has received extensive
attention from researchers in industrial relations and economics. Despite
a voluminous literature, controversy continues regarding the effect of unions on productivity growth. In this paper, meta-analysis and metaregression
analysis is used to quantify the association between unions and productivity growth and to accomplish a quantitative assessment of the empirical literature. The results indicate that the overall association between unions and productivity growth is negative, especially for the U.S. The search for moderator variables revealed that most of the variation in the published results is artificial and can be attributed to specification differences.

What do unions do to productivity? A meta-analysis

The impact of unions on productivity is explored using meta-analysis and meta-regression analysis. It is shown that most of the variation in published results is due to specification differences between studies. After controlling for differences between studies, a negative association between unions and productivity is established for the United Kingdom, whereas a positive association is established for the United States in general and for U.S. manufacturing.

The impact of U.S. unions on productivity : a bootstrap meta-analysis

Resampling methods are used to calculate confidence limits in a metaanalysis of the association between unions and productivity for the population of U.S. studies. The available evidence points to a positive and statistically significant association between unions and productivity in the U.S. manufacturing and education sectors, of around 10% and 7%, respectively

Aid effectiveness on accumulation: a meta study

The AEL (aid effectiveness literature) studies the macroeconomic effects of development aid using cross-country or panel data econometrics. It contains 97 papers of which 43 study whether development aid leads to increasing accumulation. The aggregate results of the 43 studies are that aid increases investment with about 25% of the aid, while most of the remaining 75% of the effect is crowded out by a fall in savings. However, these aggregate results are so variable that it is dubious if accumulation rises.

The effects of unions on labour productivity: a meta-analytic review

The impact of unions on productivity has been an important area of debate in
industrial relations and economics. The theoretical and empirical literature has produced conflicting results. In this paper, meta-analysis is used to quantify the association between unions and productivity and reach a quantitative assessment of the empirical literature. The results suggest that the union-productivity association is not invariant, and is country, industry and time specific.

Critical factors for electronic commerce success in small business : a meta study

Small to medium-sized enterprises (SMEs) contribute significantly to the national economies and to the employment levels of different countries and represent a viable source for inventions and innovations. The recent emergence of electronic commerce in the early nineties could provide different opportunities to the small business sector to overcome its inadequacies. However, in view of the electronic commerce/business (EC) literature in organisations in general and in SMEs specifically, it was observed that EC research is scarce. Therefore, this research attempts, by reviewing relevant EC literature, to develop deeper understanding about the factors influencing EC success in SMEs. The researcher found the following issues significantly influence EC success in SMEs: e-Value, e-Cost, e-Transformation, e-Product, e-Nvolvement, e-Nnovativeness, e-Competition, external e-Support, and e-Pressure. These factors are of importance to researchers, SMEs, professionals including educational institutions and policymakers in driving SMEs and EC forward.

Factors affecting e-commerce adoption by SMEs : a meta-analysis

There have been a myriad of research studies concerning SME e-Commerce adoption. However, the findings of these studies with regard to adoption factors have often been fragmented and contradictory. This paper analyses the previous research and synthesises the findings into a cohesive model of factors affecting SME adoption of e-Commerce. Eight meta-factors were identified: (1) perceived relative advantage; (2) perceived compatibility; (3) perceived complexity; (4) pressures from trading partners; (5) pressures from competitors; (6) external change agents; (7) knowledge and expertise about e-Commerce; and (8) management attitudes towards e-Commerce. These meta-factors were further grouped into three contexts: technological, environmental and organisational.

A preliminary meta-analysis of SME ebusiness journal publications: current trends and future research opportunities moving beyond adoption factor studies

Previous meta-analyses of SME-eBusiness journal research focuses on analysing adoption factors, pre-2000 articles and a small number of journals. This paper departs from this research by analysing 100 articles published between 2003 and 2006 in 41 journals on the basis of the research approaches employed, countries and eBusiness technologies studied, and research objectives focused upon. The paper presents preliminary insights into current major research trends based on this analysis, such as the predominant focus on adoption factor by many studies. It also identifies future research opportunities, and proposes a research agenda which aims to progress SME-eBusiness research beyond adoption factor studies by outlining research objectives to help SMEs overcome barriers and exploit drivers.

Fruit and vegetable consumption and stroke : meta-analysis of cohort studies

Background
Increased consumption of fruit and vegetables has been shown to be associated with a reduced risk of stroke in most epidemiological studies, although the extent of the association is uncertain. We quantitatively assessed the relation between fruit and vegetable intake and incidence of stroke in a meta-analysis of cohort studies.

Methods
We searched MEDLINE, EMBASE, the Cochrane Library, and bibliographies of retrieved articles. Studies were included if they reported relative risks and corresponding 95% CIs of stroke with respect to frequency of fruit and vegetable intake.

Findings
Eight studies, consisting of nine independent cohorts, met the inclusion criteria. These groups included 257 551 individuals (4917 stroke events) with an average follow-up of 13 years. Compared with individuals who had less than three servings of fruit and vegetables per day, the pooled relative risk of stroke was 0·89 (95% CI 0·83–0·97) for those with three to five servings per day, and 0·74 (0·69–0·79) for those with more than five servings per day. Subgroup analyses showed that fruit and vegetables had a significant protective effect on both ischaemic and haemorrhagic stroke.

Interpretation
Increased fruit and vegetable intake in the range commonly consumed is associated with a reduced risk of stroke. Our results provide strong support for the recommendations to consume more than five servings of fruit and vegetables per day, which is likely to cause a major reduction in strokes.