In recent years, big data and its potential to shed valued insights into enhanced decision-making processes have attracted increasing interest from both academia and industry.

The amount of data generated by businesses and public administrations and numerous industrial and scientific research facilities has increased immeasurably in the past years, turning traditional systems into complex or supercomplex systems. These data may be structured, semistructured, and/or unstructured, extracted from sources as different as Natural Language Processing (chatbots, comments, and social media), multimedia content (videos, images, and audio), geographic information (GIS), or sensors (Internet of Things/Everything) on a wide variety of platforms (e.g., machine-to-machine communications, social media sites, and sensors networks).

Computational intelligence techniques form a set of nature-inspired computational methodologies and techniques which have been developed to face the aforementioned complex scientific programming, for which traditional models are unable to work due to high complexity, uncertainty, and the stochastic nature of processes. These techniques typically include parallel/distributed pattern-recognition techniques, genetic programming, fuzzy systems, or evolutionary computation.

The overall aim of this special issue is to collect state-of-the-art research findings on the latest developments, as well as up-to-date issues and challenges in the field of computational intelligence applied to scientific programming. Proposed submissions should be original, unpublished, and novel in-depth research that makes significant methodological or application contributions. Review articles on the topics below are also welcome.

Potential topics include but are not limited to the following:

• Machine learning approaches for scientific programming problems related to data mining
• Programming modeling of parallel/distributed pattern-recognition strategies applied to scientific forecasting problems
• Performance modeling of computational intelligence using parallel computing for big data analytics on heterogeneous systems
• Fuzzy rule-based languages for dealing with uncertainty in processing very large data sets
• Scientific programming languages and/or packages applied to machine learning, big data analytics, and scientific computing
• Optimization applications, chaos-theory and nonlinear dynamics approaches to solve scientific programming