Advances in Genetic Programming, Volume 3Kenneth E. Kinnear, Peter J. Angeline, Lee Spector Genetic programming, a form of genetic algorithm that evolves programs and program-like executable structures, is a new paradigm for developing reliable, time- and cost-effective applications. The second volume of Advances in Genetic Programming highlights many of the most recent technical advances in this increasingly popular field. The twenty-three contributions are divided into four parts: Variations on the Genetic Programming Theme; Hierarchical, Recursive, and Pruning Genetic Programs; Analysis and Implementation Issues; and New Environments for Genetic Programming. The first part extends the core concepts of genetic programming through the addition of new evolutionary techniques -- adaptive and self-adaptive crossover methods, hill climbing operators, and the inclusion of introns into the representation. Creating more concise executable structures is a long-term research topic in genetic programming. The second part describes the field's most recent efforts, including the dynamic manipulation of automatically defined functions, evolving logic programs that generate recursive structures, and using minimum description length heuristics to determine when and how to prune evolving structures. The third part takes up the many implementation and analysis issues associated with evolving programs. Advanced applications of genetic programming to nontrivial real-world problems are described in the final part: remote sensing of pressure ridges in Arctic sea ice formations from satellite imagery, economic prediction through model evolution, the evolutionary development of stress and loading models for novel materials, and data mining of a large customer database to optimize responses to special offers. |
Contents
Geum Yong Lee roscascr siemens | 6 |
An Automatic Software ReEngineering Tool Based | 15 |
CAD Surface Reconstruction from Digitized 3D Point Data with | 41 |
Jörn Mehnen Department of Computer Science | 64 |
A Genetic Programming Approach for Robust Language Interpretation | 67 |
Automatic Synthesis Placement and Routing of Electrical Circuits | 105 |
Quantum Computing Applications of Genetic Programming | 135 |
The Evolution of Size and Shape | 163 |
Efficient Evolution of Machine Code for CISC Architectures Using | 275 |
Submachinecode Genetic Programming | 301 |
The Internal Reinforcement of Evolving Algorithms | 325 |
Forrest H Bennett III Moscow ID 838441010 | 334 |
Inductive Genetic Programming with Immune Network Dynamics | 355 |
Herbert J Bernstein Oakland CA 94610 | 375 |
A SelfTuning Mechanism for DepthDependent Crossover | 377 |
Genetic Recursive Regression for Modeling and Forecasting | 401 |
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Advances in Genetic AIM-GP AllNodes analysis Angeline Annual Conference applied approach average Banzhaf behavior binary bits building blocks capacitor catchment chapter circuit code bloat complexity component credit score Credit-Blame map crossover crossover operator D. B. Fogel defined depth ERC values evaluation evolution Evolutionary Computation Evolutionary Programming evolved program example Figure fitness function Genetic Algorithms Genetic Programming Genetic Programming 1996 GP system immune individual input Instruction Blocks introns IRNP J. R. Koza Kinnear Langdon loop machine code machine learning modifiable wire Morgan Kaufmann mutation node Nordin NP program object offspring output parallel parameters parent parsimony performance Poli population probability problem Proceedings processors quantum algorithm quantum computer qubit R. L. Riolo Eds random representation Riolo robot rooted-tree Rosca schema schemata search space Section selection solution solving for f(x structure sub-machine-code GP subtree Swap transformations Unity No-Null variable variation operators vector