The Genetic and Evolutionary Programming Laboratory provides a dedicated computational environment for developing, executing, and analysing evolutionary computation methods, with a primary focus on genetic algorithms for combinatorial optimisation and genetic programming for symbolic regression and automatic program synthesis. Built on NVIDIA RTX 6000 ADA and H100 NVL GPUs combined with high-core-count CPU infrastructure, EVOLAB is configured to support the population-level parallelism inherent in evolutionary methods, enabling large-scale fitness evaluations, multi-objective optimisation runs, and extended generational searches across complex solution spaces. EVOLAB is specifically oriented toward problems where solution spaces are non-convex, discontinuous, or analytically intractable, and where evolutionary search provides a principled alternative to gradient-based or exact methods. The platform supports research and applied use cases spanning resource allocation, scheduling, feature selection, symbolic model discovery, and the automated design of analytical pipelines. This testbed contributes to the CITADELS Framework by providing accessible infrastructure for evolutionary computation research at a scale and reproducibility that supports both academic investigation and industry-facing proof-of-concept development.