This dataset comprises the experimental results of a distributed genetic algorithm for application placement in the cloud–edge/fog continuum. It includes: - execution logs of multiple placement scenarios under four different distribution models (traditional centralized, semi-distributed, fully-distributed, neighbour-aware), each with multiple repetitions; - csv files capturing solution sets (Pareto fronts) for each run, including objective values (e.g., latency, cost, resource usage) and instance metadata (e.g., number of infrastructure nodes, number of applications/services, worker counts, distribution mode); - aggregated result plots summarising performance metrics (e.g., convergence, generational distance, network overhead) across scenarios, generated via analizeresults.py; - configuration files to reproduce the experiments (executionConfig.py, optimizationConfig.py, experimentationConfig.py).