Lecture #2: Parallel and Distributed Genetic Programming.
The successful application of Genetic Programming (GP, one of the available Evolutionary Algorithms) to optimization problems has encouraged an increasing number of researchers to apply these techniques to a large set of problems.
Given the difficulty of some problems, much effort has been applied to improving the efficiency of GP during the last few years. Among the available proposals, some ideas from parallel and distributed systems have been borrowed in order to reduce the computing time required for finding solutions.
Researchers have thus incorporated different forms of parallelism into the algorithm developing new algorithms and solving ever larger and harder problems.
This tutorial will describe state-of-the-art and in-progress research on all aspects of Parallel Genetic Programming.
