Springer - Representations for Genetic and Evolutionary Algorithms

In the study of genetic and evolutionary algorithms (GEAs), researchers often focus on operators and test problems while treating problem representation as a given. This book changes that approach by providing a deep look into how representation affects performance. This text offers a comprehensive overview of the influence that different problem representations have on GEA outcomes. It summarizes existing knowledge and explains how specific properties of a representation can change the way heuristic optimization methods work. By understanding these mechanics, readers can better grasp why certain algorithms succeed or fail based on how the problem is structured. The book covers essential properties such as redundancy, scaling, and locality. These elements are fundamental to understanding how representations impact the efficiency and success of GEAs and other heuristic optimization methods. It is an essential resource for those looking to move beyond standard practices and master the underlying mechanics of evolutionary computation.