Meta EA (Meta-Ethical Analysis) is a subfield of meta-ethics that examines the nature, status, and foundations of ethical theories and moral judgments. It investigates the meaning of ethical terms, the objectivity of moral values, and the epistemological implications of ethical claims, focusing on the underlying principles that govern moral reasoning.
Meta EA (Meta-Ethical Analysis) is a subfield of meta-ethics that examines the nature, status, and foundations of ethical theories and moral judgments. It investigates the meaning of ethical terms, the objectivity of moral values, and the epistemological implications of ethical claims, focusing on the underlying principles that govern moral reasoning.
2020, Systems Ecology Report, Institute of Terrestrial Ecology, Swiss Federal Institute of Technology ETH, Zurich, Switzerland
Die Berichte der Systemökologie sind entweder Vorabdrucke oder technische Berichte. Die Vorabdrucke sind Artikel, welche bei einer wissenschaftlichen Zeitschrift zur Publikation eingereicht worden sind; zu einem möglichst frühen Zeitpunkt... more
Die Berichte der Systemökologie sind entweder Vorabdrucke oder technische Berichte. Die Vorabdrucke sind Artikel, welche bei einer wissenschaftlichen Zeitschrift zur Publikation eingereicht worden sind; zu einem möglichst frühen Zeitpunkt sollen damit diese Arbeiten interessierten LeserInnen besser zugänglich gemacht werden. Die technischen Berichte dokumentieren erschöpfend Forschungs-und Entwicklungsresultate von allgemeinem Interesse.
Testing is the most important Quality Assurance (QA) measure which consumes a significant portion of budget, time and effort in the development process. For real time systems, temporal testing is as crucial as functional testing. An... more
Testing is the most important Quality Assurance (QA) measure which consumes a significant portion of budget, time and effort in the development process. For real time systems, temporal testing is as crucial as functional testing. An important activity in dynamic testing is the test case design. Evolutionary testing has shown promising results for the automation of test case design process at a reasonable computational cost. The disadvantage of evolutionary testing is that its time consuming and it depends on the parameter settings. Evolutionary algorithms can be used to find the optimal parameter settings of another evolutionary algorithm. In this research paper, a Meta level Evolutionary Algorithm (Meta-EA) is utilized to tune the parameters of evolutionary algorithm for Worst Case Execution Time (WCET) analysis. A number of experiments have been conducted for analysis using X32 (32-bit soft processor core implemented on FPGA) as the target hardware. Famous sorting algorithms have been used as programs under test for these experiments. Results have shown an average improvement of 25% in finding WCET by an evolutionary algorithm with tuned parameters compared to evolutionary algorithm with standard parameters. Furthermore, performance gap was found to be increasing with increase in test input size.