@inproceedings{ZowghiOffen1997, title = {A logical framework for modeling and reasoning about the evolution of requirements}, author = {Didar Zowghi and Ray Offen}, booktitle = {Proc. IEEE Int'l Symp. Requirements Engineering}, pages = {247-259}, publisher = {IEEE Press}, year = 1997, timestamp = {2008.05.15}, added = {2007-04-26 14:17:02 +0200}, owner = {pdeleenh}, modified = {2007-04-26 14:20:46 +0200}, biburl = {http://www.bibsonomy.org/bibtex/2cd06c254554c534a3043afa1dc36e208/pdeleenh}, keywords = {requirements logic engineering, software evolution,} } @phdthesis{Wuyts2001, title = {A Logic Meta-Programming Approach to Support the Co-Evolution of Object-Oriented Design and Implementation}, author = {Roel Wuyts}, month = {January}, school = {Department of Computer Science, Vrije Universiteit Brussel}, year = 2001, timestamp = {2008.05.15}, owner = {pdeleenh}, modified = {2007-09-25 21:46:17 +0200}, biburl = {http://www.bibsonomy.org/bibtex/2c99af9b9f11d21e7a7aa746bfc490ae1/pdeleenh}, keywords = {logic meta object-oriented co-evolution, programming,} } @inproceedings{TourweMens2003, title = {Identifying Refactoring Opportunities Using Logic Meta Programming}, author = {Tom {Tourw\'e} and Tom Mens}, month = {March}, pages = {91--100}, publisher = {IEEE Press}, year = 2003, timestamp = {2008.05.15}, isbn = {0-7695-1902-4}, owner = {pdeleenh}, modified = {2007-10-10 09:53:07 +0200}, biburl = {http://www.bibsonomy.org/bibtex/2be9efcb564c15d50b12d97be8a2a4a87/pdeleenh}, keywords = {meta logic software refactoring, programming evolution,} } @article{Schuerr1996, title = {Logic based programmed structure rewriting systems}, author = {Andy {Sch\"urr}}, journal = {Fundamenta Informaticae}, month = {June}, number = {3 and 4}, pages = {363--385}, publisher = {IOS Press}, volume = 26, year = 1996, timestamp = {2008.05.15}, isbn = {ISSN 0169-2968}, owner = {pdeleenh}, modified = {2007-10-15 16:28:57 +0200}, biburl = {http://www.bibsonomy.org/bibtex/2d0a2f598a2ed9a6bf7f8ed6164562306/pdeleenh}, keywords = {graph logic rewriting,} } @book{vanlambalgen:pte, title = {The proper treatment of events}, author = {M. van Lambalgen and F. Hamm}, note = {Explorations in semantics series, edited by Susan Rothstein, ISBN 1-4051-1213-1, ISBN 1-4051-1212-3}, publisher = {Blackwell Publishing}, year = 2005, biburl = {http://www.bibsonomy.org/bibtex/2d25eb3a8005a81f0bacd4c50a0f91ce9/unhammer}, keywords = {syllabus events cognition semantics logic time philosophy} } @inproceedings{MarcinkowskiP92, title = {Undecidability of the Horn-clause Implication Problem}, author = {J. Marcinkowski and L. Pacholski}, booktitle = {Proceedings of the 33rd IEEE Annual Symposium on Foundations of Computer Science}, pages = {354-362}, publisher = {IEEE}, year = 1992, url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=267755}, doi = {10.1109/SFCS.1992.267755}, description = {implication among Horn-clauses is not decidable}, biburl = {http://www.bibsonomy.org/bibtex/22fae374f9553a7a058113f971212ba58/emanuel}, keywords = {logic decidability horn-clauses} } @article{Tsakonas:Bpw:06, title = {Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming}, author = {Athanasios Tsakonas and George Dounias and Michael Doumpos and Constantin Zopounidis}, journal = {Expert Systems With Applications}, month = {April}, note = {Intelligent Information Systems for Financial Engineering}, number = 3, pages = {449--461}, volume = 30, year = 2006, doi = {doi:10.1016/j.eswa.2005.10.009}, abstract = {The paper demonstrates the efficient use of hybrid intelligent systems for solving the classification problem of bankruptcy. The aim of the study is to obtain classification schemes able to predict business failure. Previous attempts to form efficient classifiers for the same problem using intelligent or statistical techniques are discussed throughout the paper. The application of neural logic networks by means of genetic programming is proposed. This is an advantageous approach enabling the interpretation of the network structure through set of expert rules, which is a desirable feature for field experts. These evolutionary neural logic networks are consisted of an innovative hybrid intelligent methodology, by which evolutionary programming techniques are used for obtaining the best possible topology of a neural logic network. The genetic programming process is guided using a context-free grammar and indirect encoding of the neural logic networks into the genetic programming individuals. Indicative classification results are presented and discussed in detail in terms of both, classification accuracy and solution interpretability.}, biburl = {http://www.bibsonomy.org/bibtex/28e99d90b24d689f88715eb92bb801d17/brazovayeye}, keywords = {networks, Cellular encoding Bankruptcy, Neural logic programming, genetic algorithms, Grammar-Guided} } @article{Tsakonas:2004:JAL, title = {An evolutionary system for neural logic networks using genetic programming and indirect encoding}, author = {Athanasios Tsakonas and Vasilios Aggelis and Ioannis Karkazis and Georgios Dounias}, journal = {Journal of Applied Logic}, number = 3, pages = {349--379}, volume = 2, year = 2004, url = {http://www.sciencedirect.com/science/article/B758H-4C8P84V-1/2/e66a004270eeee4e1c50fa3e09ddd003}, owner = {wlangdon}, doi = {doi:10.1016/j.jal.2004.03.005}, abstract = {Nowadays, intelligent connectionist systems such as artificial neural networks have been proved very powerful in a wide area of applications. Consequently, the ability to interpret their structure was always a desirable feature for experts. In this field, the neural logic networks (NLN) by their definition are able to represent complex human logic and provide knowledge discovery. However, under contemporary methodologies, the training of these networks may often result in non-comprehensible or poorly designed structures. we propose an evolutionary system that uses current advances in genetic programming that overcome these drawbacks and produces neural logic networks that can be arbitrarily connected and are easily interpretable into expert rules. To accomplish this task, we guide the genetic programming process using a context-free grammar and we encode indirectly the neural logic networks into the genetic programming individuals. We test the proposed system in two problems of medical diagnosis. Our results are examined both in terms of the solution interpretability that can lead in knowledge discovery, and in terms of the achieved accuracy. We draw conclusions about the effectiveness of the system and we propose further research directions.}, biburl = {http://www.bibsonomy.org/bibtex/2b75da801e6d0841daf743fa1eb627238/brazovayeye}, keywords = {connectionist genetic diagnosis Cellular algorithms, programming, logic encoding, Coronary Neural Cardiac Symbolic artery networks, systems, Grammar-guided diagnosis, SPECT disease} } @article{Reiser:2001:AI, title = {Scaling Up Inductive Logic Programming: An Evolutionary Wrapper Approach}, author = {Philip G. K. Reiser and Patricia J. Riddle}, journal = {Applied Intelligence}, month = {November-December}, note = {Special Issue: Simulated Evolution and Learning}, number = 3, pages = {181--197}, volume = 15, year = 2001, url = {http://www.stancomb.co.uk/~prr/Papers/AppInt.ps}, issn = {0924-669X}, doi = {doi:10.1023/A:1011239013893}, size = {17 pages}, abstract = {Inductive logic programming (ILP) algorithms are classification algorithms that construct classifiers represented as logic programs. ILP algorithms have a number of attractive features, notably the ability to make use of declarative background (user-supplied) knowledge. However, ILP algorithms deal poorly with large data sets (>10000 examples) and their widespread use of the greedy set-covering algorithm renders them susceptible to local maxima in the space of logic programs. This paper presents a novel approach to address these problems based on combining the local search properties of an inductive logic programming algorithm with the global search properties of an evolutionary algorithm. The proposed algorithm may be viewed as an evolutionary wrapper around a population of ILP algorithms. The evolutionary wrapper approach is evaluated on two domains. The chess-endgame (KRK) problem is an artificial domain that is a widely used benchmark in inductive logic programming, and Part-of-Speech Tagging is a real-world problem from the field of Natural Language Processing. In the latter domain, data originates from excerpts of the Wall Street Journal. Results indicate that significant improvements in predictive accuracy can be achieved over a conventional ILP approach when data is plentiful and noisy.}, biburl = {http://www.bibsonomy.org/bibtex/2dc6d1615bf289b160eb06fc939a6e3f3/brazovayeye}, keywords = {programming, algorithms, learning, inductive genetic sampling, logic machine evolutionary ILP} } @inproceedings{reiser:1999:ELPPT, title = {Evolution of Logic Programs: Part-of-Speech Tagging}, address = {Mayflower Hotel, Washington D.C., USA}, author = {Philip G. K. Reiser and Patricia J. Riddle}, booktitle = {Proceedings of the Congress on Evolutionary Computation}, editor = {Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala}, month = {6-9 July}, pages = {1338--1346}, publisher = {IEEE Press}, volume = 2, year = 1999, url = {http://www.stancomb.co.uk/~prr/Papers/cec99.ps}, isbn = {0-7803-5537-7 (Microfiche)}, abstract = {An algorithm is presented for learning concept classification rules. It is a hybrid between evolutionary computing and inductive logic programming (ILP). Given input of positive and negative examples, the algorithm constructs a logic program to classify these examples. The algorithm has several attractive features, including the ability to use explicit background (user-supplied) knowledge and to produce comprehensible output. We present results of using the algorithm to a natural language processing problem, part-of-speech tagging. The results indicate that using an evolutionary algorithm to direct a population of ILP learners can increase accuracy. This result is further improved when crossover is used to exchange rules at intermediate stages in learning. The improvement over Progol, a greedy ILP algorithm, is statistically significant (P<0.005)}, biburl = {http://www.bibsonomy.org/bibtex/2555a009db482ff7269d484df80a8a220/brazovayeye}, keywords = {natural logic inductive algorithms, evolutionary language ILP genetic programming, processing, mining, data} } @inproceedings{pedrycz:2001:gecco, title = {Evolutionary Optimization of Logic-Oriented Systems}, address = {San Francisco, California, USA}, author = {Witold Pedrycz and Marek Reformat}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)}, editor = {Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke}, month = {7-11 July}, pages = {1389--1396}, publisher = {Morgan Kaufmann}, year = 2001, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d24.pdf}, address = {San Francisco, CA 94104, USA}, isbn = {1-55860-774-9}, biburl = {http://www.bibsonomy.org/bibtex/2b73640d826e3a1aee54daea408af60f6/brazovayeye}, keywords = {genetic programming neurons, computing, rule-based models, architectures, world fuzzy real applications, logic} } @inproceedings{Musumbu:1997:esiaialp, title = {Evolution Strategies to Improve Abstract Interpretation Algorithms for Logic Programming}, address = {Stanford University, CA, USA}, author = {Kaninda Musumbu and Kablan Barbar and Maroun Nassif}, booktitle = {Late Breaking Papers at the 1997 Genetic Programming Conference}, editor = {John R. Koza}, month = {13--16 July}, publisher = {Stanford Bookstore}, year = 1997, isbn = {0-18-206995-8}, biburl = {http://www.bibsonomy.org/bibtex/285d59a9f07c7bf3ffb0c9b0927a19cc4/brazovayeye}, keywords = {programming, logic Strategies Evolution} } @inproceedings{1277312, title = {Adaptive strategies for a semantically driven tree optimizer to control code growth}, address = {London}, author = {Bart Wyns and Luc Boullart}, booktitle = {GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation}, editor = {Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener}, month = {7-11 July}, pages = {1762--1762}, publisher = {ACM Press}, volume = 2, year = 2007, url = {http://doi.acm.org/10.1145/1276958.1277312}, address = {New York, NY, USA}, organisation = {ACM SIGEVO (formerly ISGEC)}, abstract = {In genetic programming many methods to fight growth exist. But most of these methods require one or multiple parameters to be set. Unfortunately performance strongly depends on a correct setting of each of those parameters. Recently a semantically driven tree optimiser has been developed. In this paper two adaptive strategies to choose a reasonable parameter setting for this growth limiter are presented.}, biburl = {http://www.bibsonomy.org/bibtex/2b93b17bdb5cbea4e564d0674f9e3a8a9/brazovayeye}, keywords = {logic genetic ant, programming: problems, control, fuzzy artificial Boolean algorithms, adaptive Poster,} } @article{wong:2005:GPEM, title = {Evolving Recursive Programs by Using Adaptive Grammar Based Genetic Programming}, author = {Man Leung Wong}, journal = {Genetic Programming and Evolvable Machines}, month = {December}, number = 4, pages = {421--455}, volume = 6, year = 2005, url = {http://cptra.ln.edu.hk/~mlwong/journal/gpem2005.pdf}, issn = {1389-2576}, doi = {doi:10.1007/s10710-005-4805-8}, size = {35 pages}, abstract = {Genetic programming (GP) extends traditional genetic algorithms to automatically induce computer programs. GP has been applied in a wide range of applications such as software re-engineering, electrical circuits synthesis, knowledge engineering, and data mining. One of the most important and challenging research areas in GP is the investigation of ways to successfully evolve recursive programs. A recursive program is one that calls itself either directly or indirectly through other programs. Because recursions lead to compact and general programs and provide a mechanism for reusing program code, they facilitate GP to solve larger and more complicated problems. Nevertheless, it is commonly agreed that the recursive program learning problem is very difficult for GP. In this paper, we propose techniques to tackle the difficulties in learning recursive programs. The techniques are incorporated into an adaptive Grammar Based Genetic Programming system (adaptive GBGP). A number of experiments have been performed to demonstrate that the system improves the effectiveness and efficiency in evolving recursive programs.}, biburl = {http://www.bibsonomy.org/bibtex/2c4d592ae10e03c8c4ce9cb8a88fd651d/brazovayeye}, keywords = {logic recursive grammars, programming, grammar genetic programs algorithms, based} } @article{Wong:2001:DSS, title = {A Flexible Knowledge Discovery System using Genetic Programming and Logic Grammars}, author = {Man Leung Wong}, journal = {Decision Support Systems}, pages = {405--428}, volume = 31, year = 2001, url = {http://www.sciencedirect.com/science/article/B6V8S-43W051G-2/2/e504e5d59385b792e3c424bd5bb4d003}, doi = {doi:10.1016/S0167-9236(01)00092-6}, abstract = {As the computing world moves from the information age into the knowledge-based age, it is beneficial to induce knowledge from the information super highway formed from the Internet and intranet. The knowledge acquired can be expressed in different knowledge representations such as computer programs, first-order logical relations, or Fuzzy Petri Nets (FPNs). In this paper, we present a flexible knowledge discovery system called GGP (Generic Genetic Programming) that applies genetic programming and logic grammars to learn knowledge in various knowledge representation formalisms. An experiment is performed to demonstrate that GGP can discover knowledge represented in FPNs that support fuzzy and approximate reasoning. To evaluate the performance of GGP in producing good FPNs, the classification accuracy of the fuzzy Petri net induced by GGP and that of the decision tree generated by C4.5 are compared. Moreover, the performance of GGP in inducing logic programs from noisy examples is evaluated. A detailed comparison to FOIL, a system that induces logic programs, has been conducted. These experiments demonstrate that GGP is a promising alternative to other knowledge discovery systems and sometimes is superior for handling noisy and inexact data.}, biburl = {http://www.bibsonomy.org/bibtex/2ab5867fdb1a71d1e3299255b61367f03/brazovayeye}, keywords = {in Petri Grammars, programming, Logic Nets genetic algorithms, Discovery Knowledge Fuzzy Databases,} } @article{ManLeungWong:1997:epidlg, title = {Evolutionary Program Induction Directed by Logic Grammars}, author = {Man Leung Wong and Kwong Sak Leung}, journal = {Evolutionary Computation}, month = {summer}, number = 2, pages = {143--180}, volume = 5, year = 1997, url = {http://www.mitpressjournals.org/doi/pdfplus/10.1162/evco.1997.5.2.143}, doi = {doi:10.1162/evco.1997.5.2.143}, size = {39 pages}, abstract = {Program induction generates a computer program that can produce the desired behavior for a given set of situations. Two of the approaches in program induction are inductive logic programming (ILP) and genetic programming (GP). Since their formalisms are so different, these two approaches cannot be integrated easily, although they share many common goals and functionalities. A unification will greatly enhance their problem-solving power. Moreover, they are restricted in the computer languages in which programs can be induced. In this paper, we present a flexible system called LOGENPRO (The LOgic grammar-based GENetic PROgramming system) that uses some of the techniques of GP and ILP. It is based on a formalism of logic grammars. The system applies logic grammars to control the evolution of programs in various programming languages and represent context-sensitive information and domain-dependent knowledge. Experiments have been performed to demonstrate that LOGENPRO can emulate GP and GP with automatically defined functions (ADFs). Moreover, LOGENPRO can employ knowledge such as argument types in a unified framework. The experiments show that LOGENPRO has superior performance to that of GP and GP with ADFs when more domain-dependent knowledge is available. We have applied LOGENPRO to evolve general recursive functions for the even-n-parity from noisy training examples. A number of experiments have been performed to determine the impact of domain-specific knowledge and noise in training examples on the speed of learning.}, biburl = {http://www.bibsonomy.org/bibtex/21219af945a9864cfb41e85e7d83e5cd7/brazovayeye}, keywords = {logic grammars genetic learning, algorithms, programming, Machine} } @inproceedings{vassilev99digital, title = {Digital Circuit Evolution and Fitness Landscapes}, address = {Mayflower Hotel, Washington D.C., USA}, author = {Vesselin K. Vassilev and Julian F. Miller and Terence C. Fogarty}, booktitle = {Proceedings of the Congress on Evolutionary Computation}, editor = {Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala}, month = {6-9 July}, publisher = {IEEE Press}, volume = 2, year = 1999, isbn = {0-7803-5537-7 (Microfiche)}, abstract = {We study the fitness landscapes generated by evolving digital circuits using an idealised model of a field-programmable gate array. It appears that the fitness landscapes of this engineering problem are quite different from many recently studied landscapes, often defined over simplified combinatorial and optimisation problems. The difference stems from the genotype representation which allows us to evolve the functionality and connectivity of an array of logic cells. Here, the genotypes are sequences which are defined over two completely different alphabets. We propose a model for studying the structure of these landscapes and measure correlation characteristics of the landscapes. It is furthermore shown that the evolutionary search can be improved when the results of the analysis are taken into account}, biburl = {http://www.bibsonomy.org/bibtex/295b134718f23cf80c7ca46820e4397fa/brazovayeye}, keywords = {circuit idealised engineering optimisation, fitness digital field-programmable evolution, programmable combinatorial problems, optimisation sequences field computation, circuits, genotype landscapes, representation, connectivity, functionality, logic search, cell correlation gate alphabets, characteristics, array, evolutionary sequences, problem, arrays, model,} } @mastersthesis{GILP1997Tveit, title = {Genetic Inductive Logic Programming}, address = {IDI/NTNU, N-7491 Trondheim, Norway}, author = {Amund Tveit}, school = {Norwegian University of Science and Technology}, type = {MSc Thesis}, year = 1997, url = {http://amundtveit.info/publications/1997/MScThesisAbstract.php}, email = {ae@amundtveit.info}, size = {179KB}, abstract = {The most used method of finding logical rules from data, inductive logic programming (ILP), has shown successful, but unfortunately not very scalable with increasing problem size. In this report a model for doing induction of logical rules, using the concepts of the potentially more scalable method of genetic algorithm, is suggested. Five strategies of reducing the search space in the representation are suggested: pruning by logical entailment, pruning by integrity constraints, pruning by logic factorisation, pruning by range restriction, and pruning using a heuristic fitness function on the cohesion of literals. The genetic operators suggested are applying these pruning search strategies. The model has yet to be implemented and tried out in an experimental setting.}, biburl = {http://www.bibsonomy.org/bibtex/2fb8fd3b324fd309247a8c2aba9d37d33/brazovayeye}, keywords = {algorithms, logic genetic ILP programming, inductive} } @article{ross:2001:ngc, title = {Logic-based Genetic Programming with Definite Clause Translation Grammars}, author = {Brian J. Ross}, journal = {New Generation Computing}, number = 4, pages = {313--337}, volume = 19, year = 2001, url = {http://citeseer.ist.psu.edu/331862.html}, notes = {http://www.ohmsha.co.jp/ngc/}, abstract = {DCTG-GP is a genetic programming system that uses definite clause translation grammars. A DCTG is a logical version of an attribute grammar that supports the definition of context--free languages, and it allows semantic information associated with a language to be easily accommodated by the grammar. This is useful in genetic programming for defining the interpreter of a target language, or incorporating both syntactic and semantic problem-specific constraints into the evolutionary search. The DCTG-GP system improves on other grammar-based GP systems by permitting non--trivial semantic aspects of the language to be defined with the grammar. It also automatically analyses grammar rules in order to determine their minimal depth and termination characteristics, which are required when generating random program trees of varied shapes and sizes. An application using DCTG-GP is described.}, biburl = {http://www.bibsonomy.org/bibtex/234acc175f61f701dc43ea2946a354a7a/brazovayeye}, keywords = {clause Programming, genetic algorithms, Logic Definite programming, translation Clause Evolutionary Grammar, Stochastic Language definite Computation, Inference Translation grammars, Prolog,} } @inproceedings{1144145, title = {A hybridized genetic parallel programming based logic circuit synthesizer}, address = {Seattle, Washington, USA}, author = {Wai Shing Lau and Kin Hong Lee and Kwong Sak Leung}, booktitle = {{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation}, editor = {Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens}, month = {8-12 July}, pages = {839--846}, publisher = {ACM Press}, volume = 1, year = 2006, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p839.pdf}, address = {New York, NY, 10286-1405, USA}, isbn = {1-59593-186-4}, organisation = {ACM SIGEVO (formerly ISGEC)}, doi = {doi:10.1145/1143997.1144145}, biburl = {http://www.bibsonomy.org/bibtex/295f825acc515656013c1d4dfd54a32bc/brazovayeye}, keywords = {mapping performance array, and aids, synthesiser, programming genetic logic hybridised programmable flowMap, look programming, design algorithms, a table, experimentation, gate up circuit technology parallel field} }