Parallel and Distributed Computational Intelligence [recurso electrónico] / edited by Francisco Fernández Vega, Erick Cantú-Paz.

When Huge Is Routine: Scaling Genetic Algorithms and Estimation of Distribution Algorithms via Data-Intensive Computing -- Evolvable Agents: A Framework for Peer-to-Peer Evolutionary Algorithms -- Evolutionary Algorithms on Volunteer Computing Platforms: The MilkyWay@Home Project -- Self-coordinated On-Chip Parallel Computing: A Swarm Intelligence Approach -- Large Scale Bioinformatics Data Mining with Parallel Genetic Programming on Graphics Processing Units -- A Review on Parallel Estimation of Distribution Algorithms -- Parallel Multi-objective Optimization Using Self-organized Heterogeneous Resources -- The Role of Explicit Niching and Communication Messages in Distributed Evolutionary Multi-objective Optimization -- Adaptive Scheduling Algorithms for the Dynamic Distribution and Parallel Execution of Spatial Agent-Based Models -- On the Use of Distributed Genetic Algorithms for the Tuning of Fuzzy Rule Based-Systems -- Parallel and Distributed Optimization of Dynamic Data Structures for Multimedia Embedded Systems -- A Grid-Based Hybrid Hierarchical Genetic Algorithm for Protein Structure Prediction -- Laser Dynamics Modelling and Simulation: An Application of Dynamic Load Balancing of Parallel Cellular Automata.

The growing success of biologically inspired algorithms in solving large and complex problems has spawned many interesting areas of research. Over the years, one of the mainstays in bio-inspired research has been the exploitation of parallel and distributed environments to speedup computations and to enrich the algorithms. From the early days of research on bio-inspired algorithms, their inherently parallel nature was recognized and different parallelization approaches have been explored. Parallel algorithms promise reductions in execution time and open the door to solve increasingly larger problems. But parallel platforms also inspire new bio-inspired parallel algorithms that, while similar to their sequential counterparts, explore search spaces differently and offer improvements in solution quality. The objective in editing this book was to assemble a sample of the best work in parallel and distributed biologically inspired algorithms. The editors invited researchers in different domains to submit their work. They aimed to include diverse topics to appeal to a wide audience. Some of the chapters summarize work that has been ongoing for several years, while others describe more recent exploratory work. Collectively, these works offer a global snapshot of the most recent efforts of bioinspired algorithms’ researchers aiming at profiting from parallel and distributed computer architectures—including GPUs, Clusters, Grids, volunteer computing and p2p networks as well as multi-core processors. This volume will be of value to a wide set of readers, including, but not limited to specialists in Bioinspired Algorithms, Parallel and Distributed Computing, as well as computer science students trying to figure out new paths towards the future of computational intelligence.

19