http://www.bibsonomy.org/burst/tag/logicBibSonomy BuRST Feed for /tag/logic2008-07-27T03:37:07+02:00http://www.bibsonomy.org/bibtex/2e759a020730806fdbb3ca587c3d48ef3/snauthsnauth2008-07-13T11:15:48+02:00logic architecture,design,space,spatial <span style="color:#555555;">Apiradee <a href="http://www.bibsonomy.org/author/Kasemsook">Kasemsook</a> </span><em>London, </em>(<em>2003</em>)Sun Jul 13 11:15:48 CEST 2008ProceedingsSpatial and functional differentiation: a symbiotic and systematic relationship2003logic architecture,design,space,spatial http://www.bibsonomy.org/bibtex/2cd06c254554c534a3043afa1dc36e208/pdeleenhpdeleenh2008-07-07T16:45:32+02:00engineering, software evolution, requirements logic <span style="color:#555555;">Didar <a href="http://www.bibsonomy.org/author/Zowghi">Zowghi</a> and Ray <a href="http://www.bibsonomy.org/author/Offen">Offen</a> </span><em>Proc. IEEE Int'l Symp. Requirements Engineering, </em><em>page247-259. </em><em>IEEE Press, </em>(<em>1997</em>)Mon Jul 07 16:45:32 CEST 2008Proc. IEEE Int'l Symp. Requirements Engineering247-259A logical framework for modeling and reasoning about the evolution of requirements1997engineering, software evolution, requirements logic http://www.bibsonomy.org/bibtex/2c99af9b9f11d21e7a7aa746bfc490ae1/pdeleenhpdeleenh2008-07-07T16:45:32+02:00logic object-oriented meta programming, co-evolution, <span style="color:#555555;">Roel <a href="http://www.bibsonomy.org/author/Wuyts">Wuyts</a> </span><em>Department of Computer Science, Vrije Universiteit Brussel, </em><em>January2001. </em>Mon Jul 07 16:45:32 CEST 2008JanuaryA Logic Meta-Programming Approach to Support the Co-Evolution of Object-Oriented Design and Implementation2001logic object-oriented meta programming, co-evolution, http://www.bibsonomy.org/bibtex/2be9efcb564c15d50b12d97be8a2a4a87/pdeleenhpdeleenh2008-07-07T16:45:32+02:00refactoring, programming meta evolution, logic software <span style="color:#555555;">Tom <a href="http://www.bibsonomy.org/author/{Tourw\&#039;e}">Tourw'e</a> and Tom <a href="http://www.bibsonomy.org/author/Mens">Mens</a> </span><em>page91--100. </em><em>IEEE Press, </em><em>March2003. </em>Mon Jul 07 16:45:32 CEST 2008March91--100Identifying Refactoring Opportunities Using Logic Meta Programming2003refactoring, programming meta evolution, logic software http://www.bibsonomy.org/bibtex/2d0a2f598a2ed9a6bf7f8ed6164562306/pdeleenhpdeleenh2008-07-07T16:45:32+02:00rewriting, graph logic <span style="color:#555555;">Andy <a href="http://www.bibsonomy.org/author/{Sch\&#034;urr}">Sch&#252;rr</a> </span><em>Fundamenta Informaticae</em><em>26(3 and 4):363--385</em><em>June1996. </em>Mon Jul 07 16:45:32 CEST 2008Fundamenta InformaticaeJune3 and 4363--385Logic based programmed structure rewriting systems261996rewriting, graph logic http://www.bibsonomy.org/bibtex/2d25eb3a8005a81f0bacd4c50a0f91ce9/unhammerunhammer2008-07-01T15:30:18+02:00events cognition semantics philosophy logic syllabus time <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/van Lambalgen">van Lambalgen</a> and F. <a href="http://www.bibsonomy.org/author/Hamm">Hamm</a> </span><em>Blackwell Publishing, </em>(<em>2005</em>) <em>Explorations in semantics series, edited by Susan Rothstein, ISBN 1-4051-1213-1, ISBN 1-4051-1212-3 . </em>Tue Jul 01 15:30:18 CEST 2008Explorations in semantics series, edited by Susan Rothstein, ISBN 1-4051-1213-1, ISBN 1-4051-1212-3The proper treatment of events2005events cognition semantics philosophy logic syllabus time implication among Horn-clauses is not decidablehttp://www.bibsonomy.org/bibtex/22fae374f9553a7a058113f971212ba58/emanuelemanuel2008-06-23T18:44:14+02:00decidability horn-clauses logic <span style="color:#555555;">J. <a href="http://www.bibsonomy.org/author/Marcinkowski">Marcinkowski</a> and L. <a href="http://www.bibsonomy.org/author/Pacholski">Pacholski</a> </span><em>Proceedings of the 33rd IEEE Annual Symposium on Foundations of Computer Science, </em><em>page354-362. </em><em>IEEE, </em>(<em>1992</em>)Mon Jun 23 18:44:14 CEST 2008Proceedings of the 33rd IEEE Annual Symposium on Foundations of Computer Science354-362Undecidability of the Horn-clause Implication Problem1992decidability horn-clauses logic http://www.bibsonomy.org/bibtex/28e99d90b24d689f88715eb92bb801d17/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00programming, genetic Neural encoding Cellular Grammar-Guided logic Bankruptcy, networks, algorithms, <span style="color:#555555;">Athanasios <a href="http://www.bibsonomy.org/author/Tsakonas">Tsakonas</a> and George <a href="http://www.bibsonomy.org/author/Dounias">Dounias</a> and Michael <a href="http://www.bibsonomy.org/author/Doumpos">Doumpos</a> and Constantin <a href="http://www.bibsonomy.org/author/Zopounidis">Zopounidis</a> </span><em>Expert Systems With Applications</em><em>30(3):449--461</em><em>April2006. </em><em>Intelligent Information Systems for Financial Engineering . </em>Thu Jun 19 17:46:40 CEST 2008Expert Systems With ApplicationsAprilIntelligent Information Systems for Financial Engineering3449--461Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming302006programming, genetic Neural encoding Cellular Grammar-Guided logic Bankruptcy, networks, algorithms, 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.http://www.bibsonomy.org/bibtex/2b75da801e6d0841daf743fa1eb627238/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00genetic Cardiac Neural SPECT programming, diagnosis Symbolic artery systems, Coronary Cellular encoding, disease logic Grammar-guided diagnosis, networks, connectionist algorithms, <span style="color:#555555;">Athanasios <a href="http://www.bibsonomy.org/author/Tsakonas">Tsakonas</a> and Vasilios <a href="http://www.bibsonomy.org/author/Aggelis">Aggelis</a> and Ioannis <a href="http://www.bibsonomy.org/author/Karkazis">Karkazis</a> and Georgios <a href="http://www.bibsonomy.org/author/Dounias">Dounias</a> </span><em>Journal of Applied Logic</em><em>2(3):349--379</em>(<em>2004</em>)Thu Jun 19 17:46:40 CEST 2008Journal of Applied Logic3349--379An evolutionary system for neural logic networks using genetic programming and indirect encoding22004genetic Cardiac Neural SPECT programming, diagnosis Symbolic artery systems, Coronary Cellular encoding, disease logic Grammar-guided diagnosis, networks, connectionist algorithms, 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.http://www.bibsonomy.org/bibtex/2dc6d1615bf289b160eb06fc939a6e3f3/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00programming, learning, inductive evolutionary machine logic algorithms, ILP sampling, genetic <span style="color:#555555;">Philip G. K. <a href="http://www.bibsonomy.org/author/Reiser">Reiser</a> and Patricia J. <a href="http://www.bibsonomy.org/author/Riddle">Riddle</a> </span><em>Applied Intelligence</em><em>15(3):181--197</em><em>November-December2001. </em><em>Special Issue: Simulated Evolution and Learning . </em>Thu Jun 19 17:46:40 CEST 2008Applied IntelligenceNovember-DecemberSpecial Issue: Simulated Evolution and Learning3181--197Scaling Up Inductive Logic Programming: An Evolutionary Wrapper Approach152001programming, learning, inductive evolutionary machine logic algorithms, ILP sampling, genetic 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.http://www.bibsonomy.org/bibtex/2555a009db482ff7269d484df80a8a220/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00genetic inductive processing, algorithms, language programming, data evolutionary ILP logic mining, natural <span style="color:#555555;">Philip G. K. <a href="http://www.bibsonomy.org/author/Reiser">Reiser</a> and Patricia J. <a href="http://www.bibsonomy.org/author/Riddle">Riddle</a> </span><em>Proceedings of the Congress on Evolutionary Computation, </em><em>2, </em><em>page1338--1346. </em><em>Mayflower Hotel, Washington D.C., USA, </em><em>IEEE Press, </em><em>6-9 July1999. </em>Thu Jun 19 17:46:40 CEST 2008Mayflower Hotel, Washington D.C., USAProceedings of the Congress on Evolutionary Computation6-9 July1338--1346Evolution of Logic Programs: Part-of-Speech Tagging21999genetic inductive processing, algorithms, language programming, data evolutionary ILP logic mining, natural 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)http://www.bibsonomy.org/bibtex/2b73640d826e3a1aee54daea408af60f6/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00computing, models, applications, real neurons, architectures, rule-based programming logic fuzzy world genetic <span style="color:#555555;">Witold <a href="http://www.bibsonomy.org/author/Pedrycz">Pedrycz</a> and Marek <a href="http://www.bibsonomy.org/author/Reformat">Reformat</a> </span><em>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), </em><em>page1389--1396. </em><em>San Francisco, California, USA, </em><em>Morgan Kaufmann, </em><em>7-11 July2001. </em>Thu Jun 19 17:46:40 CEST 2008San Francisco, California, USAProceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)7-11 July1389--1396Evolutionary Optimization of Logic-Oriented Systems2001computing, models, applications, real neurons, architectures, rule-based programming logic fuzzy world genetic http://www.bibsonomy.org/bibtex/285d59a9f07c7bf3ffb0c9b0927a19cc4/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00programming, Strategies logic Evolution <span style="color:#555555;">Kaninda <a href="http://www.bibsonomy.org/author/Musumbu">Musumbu</a> and Kablan <a href="http://www.bibsonomy.org/author/Barbar">Barbar</a> and Maroun <a href="http://www.bibsonomy.org/author/Nassif">Nassif</a> </span><em>Late Breaking Papers at the 1997 Genetic Programming Conference, </em><em>Stanford University, CA, USA, </em><em>Stanford Bookstore, </em><em>13--16 July1997. </em>Thu Jun 19 17:46:40 CEST 2008Stanford University, CA, USALate Breaking Papers at the 1997 Genetic Programming Conference13--16 JulyEvolution Strategies to Improve Abstract Interpretation Algorithms for Logic Programming1997programming, Strategies logic Evolution http://www.bibsonomy.org/bibtex/2b93b17bdb5cbea4e564d0674f9e3a8a9/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00problems, ant, control, artificial Poster, programming: adaptive logic algorithms, genetic fuzzy Boolean <span style="color:#555555;">Bart <a href="http://www.bibsonomy.org/author/Wyns">Wyns</a> and Luc <a href="http://www.bibsonomy.org/author/Boullart">Boullart</a> </span><em>GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, </em><em>2, </em><em>page1762--1762. </em><em>London, </em><em>ACM Press, </em><em>7-11 July2007. </em>Thu Jun 19 17:35:00 CEST 2008LondonGECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation7-11 July1762--1762Adaptive strategies for a semantically driven tree optimizer to control code growth22007problems, ant, control, artificial Poster, programming: adaptive logic algorithms, genetic fuzzy Boolean 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.http://www.bibsonomy.org/bibtex/2c4d592ae10e03c8c4ce9cb8a88fd651d/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00based grammar programs genetic algorithms, recursive programming, logic grammars, <span style="color:#555555;">Man Leung <a href="http://www.bibsonomy.org/author/Wong">Wong</a> </span><em>Genetic Programming and Evolvable Machines</em><em>6(4):421--455</em><em>December2005. </em>Thu Jun 19 17:35:00 CEST 2008Genetic Programming and Evolvable MachinesDecember4421--455Evolving Recursive Programs by Using Adaptive Grammar Based Genetic Programming62005based grammar programs genetic algorithms, recursive programming, logic grammars, 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.http://www.bibsonomy.org/bibtex/2ab5867fdb1a71d1e3299255b61367f03/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00genetic algorithms, in Discovery Nets Logic Grammars, Petri Knowledge programming, Databases, Fuzzy <span style="color:#555555;">Man Leung <a href="http://www.bibsonomy.org/author/Wong">Wong</a> </span><em>Decision Support Systems</em>(<em>2001</em>)Thu Jun 19 17:35:00 CEST 2008Decision Support Systems405--428A Flexible Knowledge Discovery System using Genetic Programming and Logic Grammars312001genetic algorithms, in Discovery Nets Logic Grammars, Petri Knowledge programming, Databases, Fuzzy 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.http://www.bibsonomy.org/bibtex/21219af945a9864cfb41e85e7d83e5cd7/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00logic grammars Machine learning, programming, algorithms, genetic <span style="color:#555555;">Man Leung <a href="http://www.bibsonomy.org/author/Wong">Wong</a> and Kwong Sak <a href="http://www.bibsonomy.org/author/Leung">Leung</a> </span><em>Evolutionary Computation</em><em>5(2):143--180</em><em>summer1997. </em>Thu Jun 19 17:35:00 CEST 2008Evolutionary Computationsummer2143--180Evolutionary Program Induction Directed by Logic Grammars51997logic grammars Machine learning, programming, algorithms, genetic 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.http://www.bibsonomy.org/bibtex/295b134718f23cf80c7ca46820e4397fa/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00circuit field optimisation problems, search, field-programmable array, sequences, gate idealised combinatorial evolution, problem, correlation evolutionary landscapes, logic optimisation, engineering computation, characteristics, genotype model, connectivity, sequences circuits, alphabets, programmable digital representation, fitness functionality, arrays, cell <span style="color:#555555;">Vesselin K. <a href="http://www.bibsonomy.org/author/Vassilev">Vassilev</a> and Julian F. <a href="http://www.bibsonomy.org/author/Miller">Miller</a> and Terence C. <a href="http://www.bibsonomy.org/author/Fogarty">Fogarty</a> </span><em>Proceedings of the Congress on Evolutionary Computation, </em><em>2, </em><em>Mayflower Hotel, Washington D.C., USA, </em><em>IEEE Press, </em><em>6-9 July1999. </em>Thu Jun 19 17:35:00 CEST 2008Mayflower Hotel, Washington D.C., USAProceedings of the Congress on Evolutionary Computation6-9 JulyDigital Circuit Evolution and Fitness Landscapes21999circuit field optimisation problems, search, field-programmable array, sequences, gate idealised combinatorial evolution, problem, correlation evolutionary landscapes, logic optimisation, engineering computation, characteristics, genotype model, connectivity, sequences circuits, alphabets, programmable digital representation, fitness functionality, arrays, cell 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 accounthttp://www.bibsonomy.org/bibtex/2fb8fd3b324fd309247a8c2aba9d37d33/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00logic ILP genetic programming, inductive algorithms, <span style="color:#555555;">Amund <a href="http://www.bibsonomy.org/author/Tveit">Tveit</a> </span>(<em>1997</em>)Thu Jun 19 17:35:00 CEST 2008IDI/NTNU, N-7491 Trondheim, NorwayGenetic Inductive Logic ProgrammingMSc Thesis1997logic ILP genetic programming, inductive algorithms, 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.http://www.bibsonomy.org/bibtex/234acc175f61f701dc43ea2946a354a7a/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00translation Logic Computation, programming, algorithms, clause Evolutionary genetic Definite Prolog, Translation Grammar, Programming, Clause Stochastic Inference grammars, definite Language <span style="color:#555555;">Brian J. <a href="http://www.bibsonomy.org/author/Ross">Ross</a> </span><em>New Generation Computing</em><em>19(4):313--337</em>(<em>2001</em>)Thu Jun 19 17:35:00 CEST 2008New Generation Computing4313--337Logic-based Genetic Programming with Definite Clause Translation Grammars192001translation Logic Computation, programming, algorithms, clause Evolutionary genetic Definite Prolog, Translation Grammar, Programming, Clause Stochastic Inference grammars, definite Language 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.