Search

At universities where students enjoy flexibility in selecting courses, the Registrar's office aims to generate an appropriate exam schedule for numerous courses and large number of students. An appropriate exam schedule should show fairness towards all students, respecting criteria and constraints: (a) eliminating or minimize the number of simultaneous exams; (b) minimize the number of consecutive exams; (c) minimize the number of multiple exams a student has per day; (d) exams should fit in rooms with predefined capacity; and (e) the number of exam periods is limited. These constraints are conflicting in nature. Hence, finding an optimal solution is challenging and the problem of exam scheduling is an NP-complete problem. Solving this problem in a reasonable amount of time requires the use of heuristic approaches. A good heuristic algorithm should aim to minimize the above mentioned constraints. In this work, we develop an evolutionary algorithm based on the scatter search approach for finding good suboptimal solutions for exam scheduling. This approach is based on maintaining a population of solutions for the purpose of generating new trial solutions. We perform experimental evaluation of our suggested algorithm on real data and compare our results with the registrar's manual schedule in addition to other optimization heuristic algorithms: Simulated Annealing, Genetic Algorithm, Three Phase Simulated Annealing (3PSA); a clustering based algorithm (FESP), and a hybrid algorithm (FESPSA).Our experimental results show that our adapted scatter search algorithm generated results that are better than FESP, 3PSA, FESPSA algorithms and the registrar's manual schedule, and it is comparable with the results generated by GA and SA.

Scatter search is an evolutionary algorithm used to solve a multitude of real-life problems. It can be classified as a population-based methodology in the field of meta-heuristics. The main characteristic of scatter search is that it attempts to find improved solutions by combining others which are non-randomly generated. The scatter search methodology provides a general template consisting of five methods which can be fine-tuned to fit the specific problem under consideration. This flexibility has allowed the application of the scatter search to many combinatorial and optimization problems. One of the problems solved by the scatter search algorithm is the assignment of proctors to exams. This problem is encountered in every university at the time of final examinations when the scheduling of proctors is needed to oversee the students sitting for exams. This task has been reputed to be a very complex one because of the large number of exams and of the various constraints of this problem. Furthermore, when done manually, assigning proctors to exams is an extremely difficult and time consuming job for university staff responsible for this task and this entails that there is a need to automate the allocation of proctors to exams. The purpose of this project is to implement a computer-based decision support system that attempts to assign proctors to exams. The software was developed using Java, which is today's most advanced object-oriented language. The algorithm used is the scatter search methodology since it was found to provide good solutions for the problem under consideration. In addition, empirical results were obtained using real-life data and were found to give high-quality solutions that maximize the proctors' preferences while maintaining the fairness of the workload given to proctors.

Li, Gang., Thesis (Ph.D.)--Chinese University of Hong Kong, 2009., Includes bibliographical references (leaves 121-136)., Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web., also in Chinese., http://library.cuhk.edu.hk/record=b6075420, 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/)

Dans plusieurs secteurs d'activité comme l'aéronautique, l'informatique, les télécommunications, l'environnement, le transport et autres, les décideurs sont confrontés à des problèmes de complexité grandissante. Il peut s'agir d'optimiser le trajet d'un véhicule, de minimiser des coûts de production, de supporter la prise de décision, d'améliorer les performances d'un circuit électronique ou encore d'ordonnancer les processus dans un système informatique. Dans de nombreux cas, le problème à résoudre peut s'exprimer comme un problème d'optimisation combinatoire qui est rarement uni-objectif. En effet, la plupart des problèmes d'optimisation combinatoire rencontrés dans la pratique nécessitent l'optimisation simultanée de plusieurs objectifs souvent contradictoires. C'est par exemple le cas pour le problème d'ordonnancement de voitures. Toutefois, malgré l'intérêt indéniable d'aborder les problèmes industriels d'un point de vue multi-objectifs, plusieurs auteurs ont noté, en recensant la littérature, que les chercheurs s'attardaient principalement à des contextes théoriques de base. En contexte multi-objectifs, l'utilisation de métaheuristiques s'avère souvent d'une grande utilité. Parmi les métaheuristiques, les algorithmes évolutionnaires, et plus particulièrement les algorithmes génétiques, se sont distingués comme étant des techniques bien adaptées à la résolution de problèmes multi-objectifs notamment à cause de leur faculté à exploiter de vastes espaces de recherche et à générer des compromis multiples en une seule étape d'optimisation. Les algorithmes génétiques tentent de simuler le processus de sélection naturelle dans un environnement hostile lié au problème à résoudre en s'inspirant des théories de l'évolution proposées par Darwin et des méthodes de combinaison de gènes introduites par Mendel. Dans cette thèse, nous proposons des approches de résolution efficaces basées sur des algorithmes évolutionnaires permettant de supporter la prise de décision pour de

Πτυχιακή εργασία -- Σχολή Τεχνολογικών Εφαρμογών -- Τμήμα Πληροφορικής, 2007, The development of satisfying exam programs in tertiary education has always been one of the problems that the educational institutes are facing. Many methods were developed to solve this problem; some of them were successful while other did not have the expected results. New perspectives came up in the field, with the development of the Evolutionary Algorithms (EAs). The last three decades, EAs are gaining more and more attention from the scientists in solving difficult and complex problems. The confrontation of the exam-timetabling problem using EAs started with the observation that this problem can be simulated by and transformed into a graph colouring problem, which the EAs were dealing with much success. Thus, the educational scientific community turn their interest to EAs in order to find a solution for this long-standing problem. In this thesis, an effort is being made to develop a solution based on EAs, for the exam-timetabling problem at the department of Informatics, of the Technological Educational Institute of Thessaloniki. For the achievement of qualitatively better results the capability of applying parallel Evolutionary Algorithms is given. The advantages and usability of Parallel EAs is also described in this thesis. Finally, the EA that has been developed was applied in a series of experiments, on test data, and the results were more than encouraging., Η δηµιουργία ικανοποιητικών προγραµµάτων εξετάσεων, στην τριτοβάθµια εκπαίδευση, ήταν ανέκαθεν ένα από τα προβλήµατα που αντιµετώπιζαν τα ανώτερα εκπαιδευτικά ιδρύµατα. Πολλές µέθοδοι αναπτύχθηκαν, για την επίλυσή του, άλλες µε επιτυχία και άλλες µε όχι και τόσο καλά αποτελέσµατα. Νέες προοπτικές όµως εµφανίστηκαν στο χώρο, παράλληλα µε την ανάπτυξη των Εξελικτικών Αλγορίθµων (ΕΑ) οι οποίοι τις τελευταίες τρείς δεκαετίες κερδίζουν όλο και περισσότερο την προσοχή των επιστηµόνων για την επίλυση δύσκολων και πολύπλοκων προβληµάτων. Η αντιµετώπιση του προβλήµατος του χρονοπρογραµµατισµού των εξετά- σεων από τους εξελικτικούς αλγορίθµους, ήρθε µε την παρατήρηση ότι αυτό το πρόβληµα µπορεί να παροµοιασθεί και να µετατραπεί σε πρόβληµα χρωµα- τισµού ενός γράφου (graph colouring problem), κάτι το οποίο οι ΕΑ αντιµετώ- πιζαν µε επιτυχία. Έτσι λοιπόν η εκπαιδευτική επιστηµονική κοινότητα έστρεψε το ενδιαφέρον της στους ΕΑ για να επιλύσει ένα πάγιο πρόβληµά της. Στην παρούσα εργασία γίνεται µία προσπάθεια ανάπτυξης ενός ΕΑ για την επίλυση αυτού του προβλήµατος στο τµήµα Πληροφορικής του Τεχνολογικού Εκπαιδευτικού Ιδρύµατος της Θεσσαλονίκης. Για την επίτευξη καλύτερων ποιοτικά αποτελεσµάτων δίνεται η δυνατότητα εφαρµογής Παράλληλων ΕΑ, των οποίων τα πλεονεκτήµατα και η χρησιµότητα αναφέρεται στα κείµενα που ακολουθούν. Τέλος, ο ΕΑ που αναπτύχθηκε εφαρµόστηκε σε µία σειρά πειραµάτων, πάνω σε δοκιµαστικά δεδοµένα, και τα αποτελέσµατα που προέκυψαν ήταν κάτι παραπάνω από ενθαρρυντικά.

Summary: A cross-layer design architecture featuring a new network stack component called a controller is presented. The controller takes system status information from the protocol components and uses it to tune the behavior of the network stack to a given performance objective. A controller design strategy using a machine learning algorithm and a simulator is proposed, implemented, and tested. Results show the architecture and design strategy are capable of producing a network stack that outperforms the existing protocol stack for arbitrary performance objectives. The techniques presented give network designers the flexibility to easily tune the performance of their networks to suit their application. This cognitive networking architecture has great potential for high performance in future wireless networks., 2007, College of Engineering and Computer Science, Degree granted: Thesis (M.S.)--Florida Atlantic University, 2007., Collection: FAU Electronic Theses and Dissertations Collection

Summary: Three major problems make Genetic Programming unfeasible or impractical for real world problems. The first is the excessive time complexity.In nature the evolutionary process can take millions of years, a time frame that is clearly not acceptable for the solution of problems on a computer. In order to apply Genetic Programming to real world problems, it is essential that its efficiency be improved. The second is called overfitting (where results are inaccurate outside the training data). In a paper[36] for the Federal Reserve Bank, authors Neely and Weller state “a perennial problem with using flexible, powerful search procedures like Genetic Programming is overfitting, the finding of spurious patterns in the data. Given the well-documented tendency for the genetic program to overfit the data it is necessary to design procedures to mitigate this.” The third is the difficulty of determining optimal control parameters for the Genetic Programming process. Control parameters control the evolutionary process. They include settings such as, the size of the population and the number of generations to be run. In his book[45], Banzhaf describes this problem, “The bad news is that Genetic Programming is a young field and the effect of using various combinations of parameters is just beginning to be explored.” We address these problems by implementing and testing a number of novel techniques and improvements to the Genetic Programming process. We conduct experiments using data sets of various degrees of difficulty to demonstrate success with a high degree of statistical confidence., 2006, College of Engineering and Computer Science, Degree granted: Dissertation (Ph.D.)--Florida Atlantic University, 2006., Collection: FAU Electronic Theses and Dissertations Collection

Fok Ka Ling., Thesis (M.Phil.)--Chinese University of Hong Kong, 2005., Includes bibliographical references (leaves 96-101)., s in English and Chinese., p.1, Acknowledgement --- p.iv, Chapter 1 --- Introduction --- p.1, Chapter 1.1 --- Evolutionary Computation --- p.1, Chapter 1.2 --- Graphics Processing Unit --- p.2, Chapter 1.3 --- Objective --- p.3, Chapter 1.4 --- Contribution --- p.4, Chapter 1.5 --- Thesis Organization --- p.4, Chapter 2 --- Evolutionary Computation --- p.6, Chapter 2.1 --- Introduction --- p.6, Chapter 2.2 --- General Framework --- p.7, Chapter 2.3 --- Features of Evolutionary Algorithm --- p.8, Chapter 2.3.1 --- Widely Applicable --- p.8, Chapter 2.3.2 --- Parallelism --- p.9, Chapter 2.3.3 --- Robust to Change --- p.9, Chapter 2.4 --- Parallel and Distributed Evolutionary Algorithm --- p.9, Chapter 2.4.1 --- Global Parallel Evolutionary Algorithms --- p.10, Chapter 2.4.2 --- Fine-Grained Evolutionary Algorithms --- p.11, Chapter 2.4.3 --- Island Distributed Evolutionary Algorithms --- p.12, Chapter 2.5 --- Summary --- p.14, Chapter 3 --- Graphics Processing Unit --- p.15, Chapter 3.1 --- Introduction --- p.15, Chapter 3.2 --- History of GPU --- p.16, Chapter 3.2.1 --- First-Generation GPUs --- p.16, Chapter 3.2.2 --- Second-Generation GPUs --- p.17, Chapter 3.2.3 --- Third-Generation GPUs --- p.17, Chapter 3.2.4 --- Fourth-Generation GPUs --- p.17, Chapter 3.3 --- The Graphics Pipelining --- p.18, Chapter 3.3.1 --- Standard Graphics Pipeline --- p.18, Chapter 3.3.2 --- Programmable Graphics Pipeline --- p.18, Chapter 3.3.3 --- Fragment Processors for Scientific Computation --- p.21, Chapter 3.4 --- GPU-CPU Analogy --- p.23, Chapter 3.4.1 --- Memory Architecture --- p.23, Chapter 3.4.2 --- Processing Model --- p.24, Chapter 3.5 --- Limitation of GPU --- p.24, Chapter 3.5.1 --- Limited Input and Output --- p.24, Chapter 3.5.2 --- Slow Data Readback --- p.24, Chapter 3.5.3 --- No Random Number Generator --- p.25, Chapter 3.6 --- Summary --- p.25, Chapter 4 --- Evolutionary Programming on GPU --- p.26, Chapter 4.1 --- Introduction --- p.26, Chapter 4.2 --- Evolutionary Programming --- p.26, Chapter 4.3 --- Data Organization --- p.29, Chapter 4.4 --- Fitness Evaluation --- p.31, Chapter 4.4.1 --- Introduction --- p.31, Chapter 4.4.2 --- Different Forms of Fitness Function --- p.32, Chapter 4.4.3 --- Parallel Fitness Function Evaluation using GPU --- p.33, Chapter 4.5 --- Mutation --- p.34, Chapter 4.5.1 --- Introduction --- p.34, Chapter 4.5.2 --- Self Adaptive Mutation Operators --- p.36, Chapter 4.5.3 --- Mutation on GPU --- p.37, Chapter 4.6 --- Selection for Replacement --- p.39, Chapter 4.6.1 --- Introduction --- p.39, Chapter 4.6.2 --- Classification of Selection Operator --- p.39, Chapter 4.6.3 --- q -Tournament Selection --- p.40, Chapter 4.6.4 --- Median Searching --- p.41, Chapter 4.6.5 --- Minimizing Data Transfer --- p.43, Chapter 4.7 --- Experimental Results --- p.44, Chapter 4.7.1 --- Visualization --- p.48, Chapter 4.8 --- Summary --- p.49, Chapter 5 --- Genetic Algorithm on GPU --- p.56, Chapter 5.1 --- Introduction --- p.56, Chapter 5.2 --- Canonical Genetic Algorithm --- p.57, Chapter 5.2.1 --- Parent Selection --- p.57, Chapter 5.2.2 --- Crossover and Mutation --- p.62, Chapter 5.2.3 --- Replacement --- p.63, Chapter 5.3 --- Experiment Results --- p.64, Chapter 5.4 --- Summary --- p.66, Chapter 6 --- Multi-Objective Genetic Algorithm --- p.70, Chapter 6.1 --- Introduction --- p.70, Chapter 6.2 --- Definitions --- p.71, Chapter 6.2.1 --- General MOP --- p.71, Chapter 6.2.2 --- Decision Variables --- p.71, Chapter 6.2.3 --- Constraints --- p.71, Chapter 6.2.4 --- Feasible Region --- p.72, Chapter 6.2.5 --- Optimal Solution --- p.72, Chapter 6.2.6 --- Pareto Optimum --- p.73, Chapter 6.2.7 --- Pareto Front --- p.73, Chapter 6.3 --- Multi-Objective Genetic Algorithm --- p.75, Chapter 6.3.1 --- Ranking --- p.76, Chapter 6.3.2 --- Fitness Scaling --- p.77, Chapter 6.3.3 --- Diversity Preservation --- p.77, Chapter 6.4 --- A Niched and Elitism Multi-Objective Genetic Algorithm on GPU --- p.79, Chapter 6.4.1 --- Objective Values Evaluation --- p.80, Chapter 6.4.2 --- Pairwise Pareto Dominance and Pairwise Distance --- p.81, Chapter 6.4.3 --- Fitness Assignment --- p.85, Chapter 6.4.4 --- Embedded Archiving Replacement --- p.87, Chapter 6.5 --- Experiment Result --- p.89, Chapter 6.6 --- Summary --- p.90, Chapter 7 --- Conclusion --- p.95, Bibliography --- p.96, http://library.cuhk.edu.hk/record=b5892582, 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/)

Zhong-Yao Zhu., "6th August 2002.", Thesis (Ph.D.)--Chinese University of Hong Kong, 2002., Includes bibliographical references (p. 227-239)., Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web., Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web., Mode of access: World Wide Web., s in English and Chinese., http://library.cuhk.edu.hk/record=b6073476, 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/)

Chi Wai Ho, Raymond., Thesis submitted in: December 1999., Thesis (M.Phil.)--Chinese University of Hong Kong, 2000., Includes bibliographical references (leaves 93-97)., s in English and Chinese., Chapter Chapter 1 --- Introduction --- p.7, Chapter 1.1 --- Thesis Outline --- p.10, Chapter 1.2 --- Contribution at a Glance --- p.11, Chapter Chapter 2 --- Background Concept and Related Work --- p.14, Chapter 2.1 --- Genetic Algorithms (GAs) --- p.14, Chapter 2.2 --- The Nature of GAs --- p.16, Chapter 2.3 --- The Role of Mutation --- p.17, Chapter 2.4 --- The Role of Crossover --- p.18, Chapter 2.5 --- The Roles of the Mutation and Crossover Rates --- p.19, Chapter 2.6 --- Adaptation of the Mutation and Crossover Rates --- p.19, Chapter 2.7 --- Diversity Control --- p.21, Chapter 2.8 --- Coarse-grain Parallel Genetic Algorithms --- p.25, Chapter 2.9 --- Adaptation of Migration Period --- p.26, Chapter 2.10 --- Serial and Parallel GAs --- p.27, Chapter 2.11 --- Distributed Java Machine (DJM) --- p.28, Chapter 2.12 --- Clustering --- p.30, Chapter Chapter 3 --- Adaptation of the Mutation and Crossover Rates --- p.35, Chapter 3.1 --- The Probabilistic Rule-based Adaptive Model (PRAM) --- p.35, Chapter 3.2 --- Time Complexity --- p.37, Chapter 3.3 --- Storage Complexity --- p.38, Chapter Chapter 4 --- Diversity Control --- p.39, Chapter 4.1 --- Repelling --- p.39, Chapter 4.2 --- Implementation --- p.42, Chapter 4.3 --- Lazy Repelling --- p.43, Chapter 4.4 --- Repelling and Lazy Repelling with Deterministic Crowding --- p.43, Chapter 4.5 --- Comparison of Repelling and Lazy Repelling with Recent Diversity Maintenance Models in Time Complexity --- p.44, Chapter Chapter 5 --- An Adaptive Parallel Genetic Algorithm --- p.46, Chapter 5.1 --- A Steady-State Genetic Algorithm --- p.46, Chapter 5.2 --- An Adaptive Parallel Genetic Algorithm (aPGA) --- p.47, Chapter 5.3 --- An Adaptive Parallel Genetic Algorithm for Clustering --- p.48, Chapter 5.4 --- Implementation --- p.48, Chapter 5.5 --- Time Complexity --- p.51, Chapter Chapter 6 --- Performance Evaluation of PRAM --- p.52, Chapter 6.1 --- Solution Quality --- p.58, Chapter 6.2 --- Efficiency --- p.60, Chapter 6.3 --- Discussion --- p.62, Chapter Chapter 7 --- Performance Evaluation of Repelling --- p.66, Chapter 7.1 --- Performance Comparison of Repelling and Lazy Repelling with Deterministic Crowding --- p.70, Chapter 7.2 --- Performance Comparison with Recent Diversity Maintenance Models --- p.73, Chapter 7.3 --- Performance Comparison with Serial and Parallel Gas --- p.75, Chapter Chapter 8 --- Performance Evaluation of aPGA --- p.78, Chapter 8.1 --- Scalability of Different Dimensionalities --- p.78, Chapter 8.2 --- Speedup of Schwefel's function --- p.83, Chapter 8.3 --- Solution Quality of Clustering Problems --- p.87, Chapter 8.4 --- Speedup of The Clustering Problem --- p.89, Chapter Chapter 9 --- Conclusion --- p.91, http://library.cuhk.edu.hk/record=b5890403, 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/)

by Cheung, Shing Kwong., Thesis (Ph.D.)--Chinese University of Hong Kong, 1999., Includes bibliographical references (p. 137-143)., Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web., Mode of access: World Wide Web., s in English and Chinese., http://library.cuhk.edu.hk/record=b6073193, 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/)

Summary: Many complex engineering designs have conflicting requirements that must be compromised to effect a successful product. Traditionally, the engineering approach breaks up the complex problem into smaller sub-components in known areas of study. Tradeoffs occur between the conflicting requirements and a sub-optimal design results. A new computational approach based on the evolutionary processes observed in nature is explored in this dissertation. Evolutionary algorithms provide methods to solve complex engineering problems by optimizing the entire system, rather than sub-components of the system. Three standard forms of evolutionary algorithms have been developed: evolutionary programming, genetic algorithms and evolution strategies. Mathematical and algorithmic details are described for each of these methods. In this dissertation, four engineering problems are explored using evolutionary programming and genetic algorithms. Exploiting the inherently parallel nature of evolution, a parallel version of evolutionary programming is developed and implemented on the MasPar MP-1. This parallel version is compared to a serial version of the same algorithm in the solution of a trial set of unimodal and multi-modal functions. The parallel version had significantly improved performance over the serial version of evolutionary programming. An evolutionary programming algorithm is developed for the solution of electronic part placement problems with different assembly devices. The results are compared with previously published results for genetic algorithms and show that evolutionary programming can successfully solve this class of problem using fewer genetic operators. The finite element problem is cast into an optimization problem and an evolutionary programming algorithm is developed to solve 2-D truss problems. A comparison to LU-decomposition showed that evolutionary programming can solve these problems and that it has the capability to solve the more complex nonlinear, College of Engineering and Computer Science, Collection: FAU Electronic Theses and Dissertations Collection, Thesis (Ph.D.)--Florida Atlantic University, 1997.

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/)