Applying Adaptive Evolutionary Algorithms to Hard Problems

This report is based on the work I have done for my Master Thesis project. The project as a whole consists of research done in the eld of evolutionary computation, and it is split into two distinct parts. The main theme is adaptive evolutionary algorithms. The rst part covers the research done on solving binary constraint satisfaction problems using adaptive evolutionary algorithms. This involves a comparative study on three algorithms, each of which incorporates a diier-ent adaptive tness measure to guide its search to a solution for an instance of a binary constraint satisfaction problem. The second part mainly consists of the development of a library. Its use is aimed at evolutionary algorithms in general. Furthermore, a genetic programming algorithm is contructed, that incorporates an adaptive tness measure. This construction served as a test of the usability of the library. The genetic programming algorithm has been used for experiments on different data sets from the data mining eld.