Genetic algorithmIn computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, causal inference, etc.
Participatory designParticipatory design (originally co-operative design, now often co-design) is an approach to design attempting to actively involve all stakeholders (e.g. employees, partners, customers, citizens, end users) in the design process to help ensure the result meets their needs and is usable. Participatory design is an approach which is focused on processes and procedures of design and is not a design style. The term is used in a variety of fields e.g.
Training and developmentTraining and development involve improving the effectiveness of organizations and the individuals and teams within them. Training may be viewed as related to immediate changes in organizational effectiveness via organized instruction, while development is related to the progress of longer-term organizational and employee goals. While training and development technically have differing definitions, the two are oftentimes used interchangeably and/or together.
Root cause analysisIn science and engineering, root cause analysis (RCA) is a method of problem solving used for identifying the root causes of faults or problems. It is widely used in IT operations, manufacturing, telecommunications, industrial process control, accident analysis (e.g., in aviation, rail transport, or nuclear plants), medicine (for medical diagnosis), healthcare industry (e.g., for epidemiology), etc. Root cause analysis is a form of inductive (first create a theory [root] based on empirical evidence [causes]) and deductive (test the theory [underlying causal mechanisms] with empirical data) inference.
