The three articles in this special section focus on the development of automated design of machine learning and search algorithms. There is a demand, especially from industry and business, to automate the design of machine ...

Usually, real-world time-series forecasting problems are dynamic. If such time-series are characterized by mere concept shifts, a passive approach to learning become ideal to continuously adapt the model parameters whenever ...

Optimisation has been with us since before the first humans opened their eyes to natural phenomena that inspire technological progress. Nowadays, it is quite hard to find a solver from the overpopulation of metaheuristics ...

Since its inception genetic programming, and later variations such as grammar-based genetic programming and grammatical evolution, have contributed to various domains such as classification, image processing, search-based ...

This paper extends a PSO-based nonlinear regression technique to dynamic environments whereby the induced model dynamically adjusts when an environmental change is detected. As such, this work hybridizes a PSO designed for ...

Metaheuristics have become a widely used approach for solving a variety of practical problems. The literature is full of diverse metaheuristics based on outstanding ideas and with proven excellent capabilities. Nonetheless, ...