Representations for Genetic and Evolutionary Algorithms

In the field of genetic and evolutionary algorithms (GEAs), much theory and empirical study has been heaped upon operators and test problems, but problem representation has often been taken as given. This monograph breaks with this tradition and studies a number of critical elements of a theory of representations for GEAs and applies them to the empirical study of various important idealized test functions and problems of commercial import. The book considers basic concepts of representations, such as redundancy, scaling and locality and describes how GEAs'performance is influenced. Using the developed theory representations can be analyzed and designed in a theory-guided manner. The theoretical concepts are used as examples for efficiently solving integer optimization problems and network design problems. The results show that proper representations are crucial for GEAs'success. TOC:Introduction.- Representations for Genetic and Evolutionary Algorithms.- Three Elements of a Theory of Genetic and Evolutionary Representations.- Time-Quality Framework for a Theory-Based Analysis and Design of Representations.- Analysis of Binary Representations of Integers.- Analysis of Tree Representations.- Design of Tree Representations.- Performance of Genetic and Evolutionary Algorithms on Tree Problems.- Summary, Conclusions and Future Work.- Annex: Optimal Communication Spanning Tree Text Instances