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, http://library.cuhk.edu.hk/record=b5888384, Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)