The large-scale applicability and use of evolutionary computing for complex real-life systems determined a need to ensure strong analytical and theoretical grounds. The special issue, with respect to these concerns, aims at building a bridge between probability, statistics, set oriented numerics and evolutionary computing. A strong interest for identifying new common and challenging research topics is considered, addressing both theoretical and applied aspects in highly complex systems. Uncertainty, error propagation or poor analysis and design, all may lead to catastrophic failures or losses in, for example, high-sensitivity, large scale complex systems; examples can be found by looking at financial markets, surveillance and defense systems, etc. Therefore, algorithms capable of delivering robust solutions while subject to erroneous or abnormal inputs, resilient to failures or with performance guarantees, are of a foremost importance. To this end, a collection of 13 papers is included in this issue, carefully selected from a total of 80 submissions we received. A series of high impact directions are brought into discussion like the design of efficient algorithms for highly complex systems, new algorithms, e.g. extended compact genetic algorithm, multi-objective artificial physics algorithm or service oriented algorithms, performance measures in dynamic optimization, Bayesian networks learning or highdimensional optimization. Real-world application examples are given towards the end, with a view of mobile large-scale networks and GPGPU-based track-beforedetect systems.