This monograph presents examples of best practices when combining bioinspired algorithms with parallel architectures. The book includes recent work by leading researchers in the field and offers a map with the main paths already explored and new ways towards the future.

Creating and Debugging Performance CUDA C.- Optimizing Shape Design with Distributed Parallel Genetic Programming on GPUs.- Characterizing Fault-tolerance in Genetic Algorithms and programming.- Comparison of Frameworks for Parallel Multiobjective Evolutionary Optimization in Dynamic Problems.- An Empirical Study of Parallel and Distributed Particle Swarm Optimization.- The generalized Island Model.- Genetic Programming for the Evolution of Associative Memories.- Parallel Architectures for Improving the Performance of a GA based trading System.- A Knowledge-Based Operator for a Genetic Algorithm which Optimizes the Distribution of Sparse Matrix Data.- Evolutive approaches for Variable Selection using a Non-parametric Noise Estimator.- A chemical evolutionary mechanism for instantiating service-based applications.