The objectives of this research area is the application of ideas taken from parallel, cluster and grid computing to algorithms inspired by nature and the application of Bioinspired Algorithm for solving optimization problems with special emphasis on parallel architecture and embedded systems optimization problems. Bioinspired algorithms are metaheuristics, inspired by natural phenomena in a broader sense, that have proven effective for difficult combinatorial optimization problems appearing in various industrial, economical, and scientific domains. Parallel Computer Architecture and Bioinspired Algorithms have been coming together during the last years. On one hand, the application of Bioinspired Algorithm to solve difficult problems has shown that they need high computation power and communications technology. Parallel architectures and Distributed systems have offered an interesting alternative to sequential counterparts. On the other hand, Bioinspired algorithms comprises a series of heuristics that can help to optimize a wide range of tasks required for Parallel and Distributed architectures to work efficiently. Genetic Algorithms (GAs), Genetic Programming (GP), Ant Colonies Algorithms (ACOs), Estimation of Distribution Algorithms (EDAs) or Simulated Annealing (SA) are nowadays helping computer designers on the advance of Computer Architecture, while improvement on parallel architectures are allowing to run computing intensive Bioinspired algorithms for solving other difficult problems. In particular this research area focuses in the following subjects: