Representations for Genetic and Evolutionary Algorithms
Description
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.
Reviews / Votes
From the reviews:
"This book presents an investigation into the effect that representations have on the performance of genetic and evolutionary algorithms. . The introduction is well written and gives a clear overview of the aims, contents and organization of the book. . is well presented and practitioners would find it of high value. . Overall, the reviewer believes this is a very good book. . this book is of high value for researchers interested in the field of evolutionary computation." (Gerry Leversha, The Mathematical Gazette, March, 2005)
"Much work in the GEA area has focused on the part of identifying powerful operators and the associated test problems showing their effectiveness . . The book also contains . a list of over 220 references, a list of symbols used in the book, and a two-layer index. By and large, this thoroughly investigated work is well written and nicely presented. It is self-contained in the sense that it does not require a reader have prior knowledge on GEA . ." (Zhizhang Shen, Zentralblatt MATH, Vol. 1030, 2004)
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