The Spirit of Evolutionary Algorithms

Evolutionary algorithms (EAs), which are based on a powerful principle of evolution: survival of the fittest, and which model some natural phenomena: genetic inheritance and Darwinian strife for survival, constitute an interesting category of modern heuristic search. During the last two decades there has been a growing interest in these algorithms; today, many complex software systems include at least some evolutionary component.However, the process of building an evolutionary program is still art rather than science; often it is based on the intuition and experience of the designer. In this introductory article we present some important ideas behind the construction of evolutionary algorithms. These ideas are illustrated by three test cases: the transportation problem, a particular nonlinear parameter optimization problem, and the traveling salesman problem. We conclude the paper with a brief discussion on how an evolutionary algorithm can be tuned to the problem while solving it, which may increase further efficiency of the algorithm in a significant way

The relics of the stronghold in Jankowo Dolne, Gniezno municipality, in the light of the archive sources and remote sensing data analysis

Przeprowadzona niedawno analiza danych teledetekcyjnych pozwoliła na dokładne określenie położenia zniwelowanego obecnie grodziska w Jankowie Dolnym, pow. gnieźnieński, wzmiankowanego po raz pierwszy w 1496 roku. Lokalizację silnie już zniszczonego obiektu, na wschód od wsi Jankowo w dolinie Wełny, wskazał już w 1924 roku P. Schumacher. Późniejsze próby precyzyjniejszego określenia miejsca po dawnym grodzie nie przyniosły spodziewanych rezultatów, a badacze brali pod uwagę kilka znacznie oddalonych od siebie punktów. Wiarygodnych informacji pozwalających na umiejscowienie grodziska dostarczyły ogólnodostępne dane teledetekcyjne, uzupełnione o fotografie wykonane z wykorzystaniem bezzałogowego statku powietrznego

High-Performance Graph Databases That Are Portable, Programmable, and Scale to Hundreds of Thousands of Cores

Graph databases (GDBs) are crucial in academic and industry applications. The key challenges in developing GDBs are achieving high performance, scalability, programmability, and portability. To tackle these challenges, we harness established practices from the HPC landscape to build a system that outperforms all past GDBs presented in the literature by orders of magnitude, for both OLTP and OLAP workloads. For this, we first identify and crystallize performance-critical building blocks in the GDB design, and abstract them into a portable and programmable API specification, called the Graph Database Interface (GDI), inspired by the best practices of MPI. We then use GDI to design a GDB for distributed-memory RDMA architectures. Our implementation harnesses one-sided RDMA communication and collective operations, and it offers architecture-independent theoretical performance guarantees. The resulting design achieves extreme scales of more than a hundred thousand cores. Our work will facilitate the development of next-generation extreme-scale graph databases