Cartesian Genetic Programming

ISBN-13: 9783642173097 / Angielski / Twarda / 2011 / 368 str.

Julian F. Miller

Opis

Cartesian Genetic Programming (CGP) is a highly effective and increasingly popular form of genetic programming. This text contains chapters written by the leading figures in the development and application of CGP.

Spis treści

Introduction.- Cartesian Genetic Programming.- Modular Cartesian Genetic Programming.- Self-modifying Cartesian Genetic Programming.- Evolution of Electronic Circuits.- Image Processing.- Developmental Approaches.- Artificial Neural Approaches.- Medical Applications.- Hardware Acceleration.- Control Applications.- Evolutionary Art.- Future Directions.- App. A, A Bibliography of CGP Papers.- App. B, CGP Software.

O autorze

The author has a degree in physics and a Ph.D. in nonlinear mathematics. He worked at Napier University and the University of Birmingham University, and he's now a lecturer in the Dept. of Electronics at The University of York. He has chaired many international conferences and workshops in genetic programming and evolvable hardware; he cofounded one of the key evolutionary computing journals; he has authored over 150 publications in the area; and he is the coinventor of the Cartesian GP method.

Blurb

Cartesian Genetic Programming (CGP) is a highly effective and increasingly popular form of genetic programming. It represents programs in the form of directed graphs, and a particular characteristic is that it has a highly redundant genotype–phenotype mapping, in that genes can be noncoding. It has spawned a number of new forms, each improving on the efficiency, among them modular, or embedded, CGP, and self-modifying CGP. It has been applied to many problems in both computer science and applied sciences.

 

This book contains chapters written by the leading figures in the development and application of CGP, and it will be essential reading for researchers in genetic programming and for engineers and scientists solving applications using these techniques. It will also be useful for advanced undergraduates and postgraduates seeking to understand and utilize a highly efficient form of genetic programming.