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Cartesian Genetic Programming / edited by Julian F. Miller
Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Natural Computing Series | SpringerLink BücherPublisher: Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg, 2011Description: Online-Ressource (XXII, 344p. 164 illus., 13 illus. in color, digital)ISBN:- 9783642173103
- 9781283365581
- 004.0151
- 006.31 22
- QA75.5-76.95
- QA76.623
Contents:
Summary: Julian F. MillerSummary: 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 appliPPN: PPN: 1651040109Package identifier: Produktsigel: ZDB-2-SCS
Cartesian Genetic Programming; Preface; Contents; List of Contributors; Acronyms; Chapter 1 Introduction to Evolutionary Computation and Genetic Programming; 1.1 Evolutionary Computation; 1.1.1 Origins; 1.1.2 Illustrating Evolutionary Computation: The Travelling Salesman Problem; 1.2 Genetic Programming; 1.2.1 GP Representation in LISP; 1.2.2 Linear or Machine Code Genetic Programming; 1.2.3 Grammar-Based Approaches; 1.2.4 PushGP; 1.2.5 Cartesian Graph-Based GP; 1.2.6 Bloat; References; Chapter 2 Cartesian Genetic Programming; 2.1 Origins of CGP; 2.2 General Form of CGP
2.3 Allelic Constraints2.4 Examples; 2.4.1 A Digital Circuit; 2.4.2 Mathematical Equations; 2.4.3 Art; 2.5 Decoding a CGP Genotype; 2.5.1 Algorithms for Decoding a CGP Genotype; 2.6 Evolution of CGP Genotypes; 2.6.1 Mutation; 2.6.2 Recombination; 2.6.3 Evolutionary Algorithm; 2.7 Genetic Redundancy in CGP Genotypes; 2.8 Parameter Settings for CGP; 2.9 Cyclic CGP; References; Chapter 3 Problem Decomposition in Cartesian Genetic Programming; 3.1 Introduction; 3.2 Embedded Cartesian Genetic Programming (ECGP); 3.2.1 Genotype Representation; 3.2.2 Modules; 3.2.3 Genotype Operators
3.2.4 Module Operators3.2.5 Evolutionary Strategy; 3.2.6 Benchmark Experiments; 3.3 Digital-Adders; 3.4 Symbolic Regression; 3.5 Lawnmower Problem; 3.6 Alternative ECGP Operators; 3.6.1 Cone-Based and Age-Based Module Creation; 3.6.2 Cone-Based Crossover; 3.7 Modular Cartesian Genetic Programming (MCGP); 3.7.1 Multi-level Module Hierarchy Representation; 3.7.2 Benchmark Experiments; 3.8 Multi-chromosome Cartesian Genetic Programming (MC-CGP); 3.8.1 Multi-chromosome Representation; 3.8.2 Multi-chromosome Evolutionary Strategy; 3.8.3 Benchmark Experiments; References
Chapter 4 Self-Modifying Cartesian Genetic Programming4.1 Introduction; 4.1.1 Discovering Mathematical Results Using Genetic Programming; 4.2 Overview of Self-Modification; 4.3 SMCGP and Its Relation to CGP; 4.3.1 Self-Modification Operators; 4.3.2 Computational Functions; 4.3.3 Arguments; 4.3.4 Relative Addressing; 4.3.5 Input and Output Nodes; 4.3.6 A Simple Example; 4.3.7 Discussion; 4.3.8 And Back to CGP; 4.4 Solving Computational Problems with SMCGP: Parity; 4.4.1 Definition of Fitness; 4.4.2 Results; 4.4.3 A General Solution to Computing Even-Parity
4.4.4 Why GP Cannot Solve General Parity Without Iteration4.5 SM vs GP vs GA; 4.6 Implementing Incremental Fitness Functions; 4.7 Conclusions; 4.8 Acknowledgements; References; Chapter 5 Evolution of Electronic Circuits; 5.1 Introduction; 5.2 Direct Evolution of Small Combinational Circuits; 5.2.1 Evolutionary vs Conventional Synthesis of Combinational Circuits; 5.2.2 CGP for Logic Synthesis; 5.2.3 Benchmark Problems; 5.2.4 Summary; 5.3 Multi-objective Evolution of Combinational Circuits; 5.3.1 Multi-objective Fitness Function; 5.3.2 Benchmarks; 5.3.3 Summary
5.4 Evolution of Polymorphic Circuits
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