This special issue has been applied on a wide range of problems arising in different fields. Evolutionary programming, stochastic local search algorithms, swarm intelligence, and machine learning, among others have been successfully applied to a range of optimization and classification problems which otherwise could not been tackled by traditional exact methods. More specifically, many problems arising in fields related to scientific programming have been increasingly addressed using meta-heuristic algorithms from AI: scientific workflows scheduling/assignment, task assignment in distributed/parallel systems, (large-scale) data mining, knowledge discovery, and text analysis/process/classification, among others. This special issue is devoted to both theoretical and practical aspects of scientific programming related to computational intelligence. Submissions are welcomed on scientific programming applied to optimization and artificial intelligence topics.

Potential topics include, but are not limited to:

• Meta-heuristic algorithms for scheduling optimization problems arising in scientific workflow applications
• Genetic programming to fine-tune meta-heuristic parameters
• Meta-heuristic algorithms for task assignment/scheduling in distributed systems
• Parallel and distributed meta-heuristics design and implementations
• Computational intelligence using high performance computing
• Meta-heuristic implementations under limited memory environments
• Scientific programming frameworks for optimization
• General meta-heuristic parameter scheduling using scientific programming
• Optimization applications on the cloud
• Scientific programming languages applied to artificial intelligence
• Large-scale optimization