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In this study, the performance of ordinal GA-based trading strategies is evaluated under six classes of time series model, namely, the linear ARMA model, the bilinear model, the ARCH model, the GARCH model, the threshold model and the chaotic model. The performance criteria employed are the winning probability, accumulated returns, Sharpe ratio and luck coefficient. Asymptotic test statistics for these criteria are derived. The hypothesis as to the superiority of GA over a benchmark, say, buy-and-hold, can then be tested using Monte Carlo simulation. From this rigorously-established evaluation process, we find that simple genetic algorithms can work very well in linear stochastic environments, and that they also work very well in nonlinear deterministic (chaotic) environments. However, they may perform much worse in pure nonlinear stochastic cases. These results shed light on the superior performance of GA when it is applied to the two tick-by-tick time series of foreign exchange rates: EUR/USD and USD/JPY.

We engage in a particular way the Anglo-American claim that a more performative Critical Management Studies (CMS) is needed to foster transformations in the “world out there” by putting into practice our learnings from a case study at Galpão Aplauso (GA), an NGO located in Brazil, which main role is to (re)socialize dispossessed youngsters through a critical methodology informed by anthropophagy.

Drawing upon an engaged investigation informed by both performative CMS and decoloniality from Latin America we embody a performative CMS “otherwise.” Through the engagement with GA, and corresponding disengagement with our institutions, we propose decolonial anthropophagy as a way to move beyond Eurocentric critiques of Eurocentrism and decolonial work monopolized by full-time academics.

From a decolonial perspective it is shown that the performative turn within CMS could be used as a way of bringing “critical development” and “critical knowledge” to “subalterns” and the “rest of the world” from a perspective of coloniality. An anthropofagic perspective on decoloniality and critique shows that “subalterns” have much to teach us and our institutions and represents a way to decolonize theory-practice and academic-nonacademic divides.

The critical-decolonial anthropophagic perspective put forward in this chapter may represent an opportunity for CMS to move beyond much of its Eurocentric traditions, thus enlarging its geographic and cultural references. It may offer CMS an alternative critical performativity concept from the South which enables CMS to become a “re/disconnector,” instead of a connector, between the Euro-American traditions and the “rest of the world,” and making things happen “otherwise.”

Are the learning procedures of genetic algorithms (GAs) able to generate optimal architectures for artificial neural networks (ANNs) in high frequency data? In this experimental study, GAs are used to identify the best architecture for ANNs. Additional learning is undertaken by the ANNs to forecast daily excess stock returns. No ANN architectures were able to outperform a random walk, despite the finding of non-linearity in the excess returns. This failure is attributed to the absence of suitable ANN structures and further implies that researchers need to be cautious when making inferences from ANN results that use high frequency data.

In this paper we show, by means of an example of its application to the problem of house price forecasting, an approach to attribute selection and dependence modelling utilising the Gamma Test (GT), a non-linear analysis algorithm that is described. The GT is employed in a two-stage process: first the GT drives a Genetic Algorithm (GA) to select a useful subset of features from a large dataset that we develop from eight economic statistical series of historical measures that may impact upon house price movement. Next we generate a predictive model utilising an Artificial Neural Network (ANN) trained to the Mean Squared Error (MSE) estimated by the GT, which accurately forecasts changes in the House Price Index (HPI). We present a background to the problem domain and demonstrate, based on results of this methodology, that the GT was of great utility in facilitating a GA based approach to extracting a sound predictive model from a large number of inputs in a data-point sparse real-world application.

Disassembly takes place in remanufacturing, recycling, and disposal, with a line being the best choice for automation. The disassembly line balancing problem seeks a sequence that is feasible, minimizes the number of workstations, and ensures similar idle times, as well as other end-of-life specific concerns. Finding the optimal balance is computationally intensive due to exponential growth. Combinatorial optimization methods hold promise for providing solutions to the problem, which is proven here to be NP-hard. Stochastic (genetic algorithm) and deterministic (greedy/hill-climbing hybrid heuristic) methods are presented and compared. Numerical results are obtained using a recent electronic product case study.

To operate with high efficiency and minimise the risks of power failures, power systems require careful monitoring. The availability of real-time data is crucial for assessing the performance of the grid and assisting operators in gauging the present security of the grid. Traditional supervisory control and data acquisition (SCADA)-based systems actually employed provides steady-state measurement values which are the calculation premise of State Estimation. More often, however, the power grid operates under dynamic state and SCADA measurements can lead to erroneous and inaccurate calculation results. The introduction of the phasor measurement unit (PMU) which provides real-time synchronised voltage and current phasors with very high accuracy is universally recognised as an important aspect of delivering a secure and sustainable power system. PMUs are a relatively new technology and because of their high procurement and installation costs, it is imperative to develop appropriate methodologies to determine the minimum number of PMUs as well as their strategic placements to guarantee full observability of a power system. Thus, the problem of the optimal PMU placement (OPP) is formulated as an optimisation problem subject to various constraints to minimise the number of PMUs while ensuring complete observability of the grid. In this chapter, integer linear programming (ILP), genetic algorithm (GA) and non-linear programming (NLP) constrained models of the OPP problem are presented. A new methodology is proposed to incorporate several constraints using the NLP. The optimisation methods have been written in Matlab software and verified on the standard Institute of Electrical and Electronics Engineers (IEEE) 14-bus test system to authenticate their effectiveness. This chapter targets United Nations Sustainable Development Goal 7.

There is a strong body of research that indicates that teacher quality has a stronger effect on student learning than any other school-based factor. At the same time, most teacher evaluation systems have traditionally failed to distinguish among different levels of teacher effectiveness or to link evaluation results to professional development in meaningful ways. In this chapter, we compare teacher responses in S. Korea and the United States to evaluation policies. We provide initial evidence that teachers and principals in Seoul defined “effective teachers” as those who helped manage their schools in areas such as affairs/planning, curriculum/instruction, science and technology, discipline, and extra-curricular activities. In contrast, the Michigan teachers and principals in the study were more likely to view effective teachers as those who planned instruction to meet student needs and provided evidence of student engagement and learning. In addition, educators’ notions of effective teachers seemed related to their responses to new teacher evaluation policies. In particular, the teachers in Seoul strongly resisted the new teacher evaluation policies while their counterparts in Michigan either supported the new evaluation policies or at least did not actively resist them. These differences seemed related to regulative, normative, and cultural-cognitive elements associated with the teacher evaluation policies in the jurisdictions where the teachers and principals worked.