From evolution theory to parallel and distributed genetic

Lecture #1: From Evolution Theory to Evolutionary Computation. Evolutionary computation is a subfield of artificial intelligence (more particularly computational intelligence) involving combinatorial optimization problems, which are based to some degree on the evolution of biological life in the natural world. In this tutorial we will review the source of inspiration for this metaheuristic and its capability for solving problems. We will show the main flavours within the field, and different problems that have been successfully solved employing this kind of techniques. 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.