Evolutionary program induction directed by logic grammars.

• Other Authors: Wong, Man Leung.
• Format: Others
• Language: English
• Published: Chinese University of Hong Kong 1995
• Subjects: Evolutionary programming (Computer science); Logic programming; Genetic algorithms
• Online Access: http://library.cuhk.edu.hk/record=b5888384; http://repository.lib.cuhk.edu.hk/en/item/cuhk-320737

by Wong Man Leung. === Thesis (Ph.D.)--Chinese University of Hong Kong, 1995. === Includes bibliographical references (leaves 227-236). === List of Figures --- p.iii === List of Tables --- p.vi === Chapter Chapter 1 : --- Introduction --- p.1 === Chapter 1.1. --- Automatic programming and program induction --- p.1 === Chapter 1.2. --- Motivation --- p.6 === Chapter 1.3. --- Contributions of the research --- p.8 === Chapter 1.4. --- Outline of the thesis --- p.11 === Chapter Chapter 2 : --- An Overview of Evolutionary Algorithms --- p.13 === Chapter 2.1. --- Evolutionary algorithms --- p.13 === Chapter 2.2. --- Genetic Algorithms (GAs) --- p.15 === Chapter 2.2.1. --- The canonical genetic algorithm --- p.16 === Chapter 2.2.1.1. --- Selection methods --- p.21 === Chapter 2.2.1.2. --- Recombination methods --- p.24 === Chapter 2.2.1.3. --- Inversion and Reordering --- p.27 === Chapter 2.2.2. --- Implicit parallelism and the building block hypothesis --- p.28 === Chapter 2.2.3. --- Steady state genetic algorithms --- p.32 === Chapter 2.2.4. --- Hybrid algorithms --- p.33 === Chapter 2.3. --- Genetic Programming (GP) --- p.34 === Chapter 2.3.1. --- Introduction to the traditional GP --- p.34 === Chapter 2.3.2. --- Automatic Defined Function (ADF) --- p.41 === Chapter 2.3.3. --- Module Acquisition (MA) --- p.44 === Chapter 2.3.4. --- Strongly Typed Genetic Programming (STGP) --- p.49 === Chapter 2.4. --- Evolution Strategies (ES) --- p.50 === Chapter 2.5. --- Evolutionary Programming (EP) --- p.55 === Chapter Chapter 3 : --- Inductive Logic Programming --- p.59 === Chapter 3.1. --- Inductive concept learning --- p.59 === Chapter 3.2. --- Inductive Logic Programming (ILP) --- p.62 === Chapter 3.2.1. --- Interactive ILP --- p.64 === Chapter 3.2.2. --- Empirical ILP --- p.65 === Chapter 3.3. --- Techniques and methods of ILP --- p.67 === Chapter Chapter 4 : --- Genetic Logic Programming and Applications --- p.74 === Chapter 4.1. --- Introduction --- p.74 === Chapter 4.2. --- Representations of logic programs --- p.76 === Chapter 4.3. --- Crossover of logic programs --- p.81 === Chapter 4.4. --- Genetic Logic Programming System (GLPS) --- p.87 === Chapter 4.5. --- Applications --- p.90 === Chapter 4.5.1. --- The Winston's arch problem --- p.91 === Chapter 4.5.2. --- The modified Quinlan's network reachability problem --- p.92 === Chapter 4.5.3. --- The factorial problem --- p.95 === Chapter Chapter 5 : --- The logic grammars based genetic programming system (LOGENPRO) --- p.100 === Chapter 5.1. --- Logic grammars --- p.101 === Chapter 5.2. --- Representations of programs --- p.103 === Chapter 5.3. --- Crossover of programs --- p.111 === Chapter 5.4. --- Mutation of programs --- p.126 === Chapter 5.5. --- The evolution process of LOGENPRO --- p.130 === Chapter 5.6. --- Discussion --- p.132 === Chapter Chapter 6 : --- Applications of LOGENPRO --- p.134 === Chapter 6.1. --- Learning functional programs --- p.134 === Chapter 6.1.1. --- Learning S-expressions using LOGENPRO --- p.134 === Chapter 6.1.2. --- The DOT PRODUCT problem --- p.137 === Chapter 6.1.2. --- Learning sub-functions using explicit knowledge --- p.143 === Chapter 6.2. --- Learning logic programs --- p.148 === Chapter 6.2.1. --- Learning logic programs using LOGENPRO --- p.148 === Chapter 6.2.2. --- The Winston's arch problem --- p.151 === Chapter 6.2.3. --- The modified Quinlan's network reachability problem --- p.153 === Chapter 6.2.4. --- The factorial problem --- p.154 === Chapter 6.2.5. --- Discussion --- p.155 === Chapter 6.3. --- Learning programs in C --- p.155 === Chapter Chapter 7 : --- Knowledge Discovery in Databases --- p.159 === Chapter 7.1. --- Inducing decision trees using LOGENPRO --- p.160 === Chapter 7.1.1. --- Decision trees --- p.160 === Chapter 7.1.2. --- Representing decision trees as S-expressions --- p.164 === Chapter 7.1.3. --- The credit screening problem --- p.166 === Chapter 7.1.4. --- The experiment --- p.168 === Chapter 7.2. --- Learning logic program from imperfect data --- p.174 === Chapter 7.2.1. --- The chess endgame problem --- p.177 === Chapter 7.2.2. --- The setup of experiments --- p.178 === Chapter 7.2.3. --- Comparison of LOGENPRO with FOIL --- p.180 === Chapter 7.2.4. --- Comparison of LOGENPRO with BEAM-FOIL --- p.182 === Chapter 7.2.5. --- Comparison of LOGENPRO with mFOILl --- p.183 === Chapter 7.2.6. --- Comparison of LOGENPRO with mFOIL2 --- p.184 === Chapter 7.2.7. --- Comparison of LOGENPRO with mFOIL3 --- p.185 === Chapter 7.2.8. --- Comparison of LOGENPRO with mFOIL4 --- p.186 === Chapter 7.2.9. --- Comparison of LOGENPRO with mFOIL5 --- p.187 === Chapter 7.2.10. --- Discussion --- p.188 === Chapter 7.3. --- Learning programs in Fuzzy Prolog --- p.189 === Chapter Chapter 8 : --- An Adaptive Inductive Logic Programming System --- p.192 === Chapter 8.1. --- Adaptive Inductive Logic Programming --- p.192 === Chapter 8.2. --- A generic top-down ILP algorithm --- p.196 === Chapter 8.3. --- Inducing procedural search biases --- p.200 === Chapter 8.3.1. --- The evolution process --- p.201 === Chapter 8.3.2. --- The experimentation setup --- p.202 === Chapter 8.3.3. --- Fitness calculation --- p.203 === Chapter 8.4. --- Experimentation and evaluations --- p.204 === Chapter 8.4.1. --- The member predicate --- p.205 === Chapter 8.4.2. --- The member predicate in a noisy environment --- p.205 === Chapter 8.4.3. --- The multiply predicate --- p.206 === Chapter 8.4.4. --- The uncle predicate --- p.207 === Chapter 8.5. --- Discussion --- p.208 === Chapter Chapter 9 : --- Conclusion and Future Work --- p.210 === Chapter 9.1. --- Conclusion --- p.210 === Chapter 9.2. --- Future work --- p.217 === Chapter 9.2.1. --- Applying LOGENPRO to discover knowledge from databases --- p.217 === Chapter 9.2.2. --- Learning recursive programs --- p.218 === Chapter 9.2.3. --- Applying LOGENPRO in engineering design --- p.220 === Chapter 9.2.4. --- Exploiting parallelism of evolutionary algorithms --- p.222 === Reference --- p.227 === Appendix A --- p.237