52 Selected Aspects of Natural

[1]  David W. Corne,et al.  Outperforming Buy-and-Hold with Evolved Technical Trading Rules: Daily, Weekly and Monthly Trading , 2010, EvoApplications.

[2]  David W. Corne,et al.  Discovering effective technical trading rules with genetic programming: towards robustly outperforming buy-and-hold , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[3]  Xin Yao,et al.  Innovative Batik Design with an Interactive Evolutionary Art System , 2009, Journal of Computer Science and Technology.

[4]  Joshua D. Knowles.  Closed-loop evolutionary multiobjective optimization , 2009, IEEE Computational Intelligence Magazine.

[5]  Douglas B. Kell,et al.  In silico modelling of directed evolution: Implications for experimental design and stepwise evolution. , 2009, Journal of theoretical biology.

[6]  D. Kell,et al.  Array-based evolution of DNA aptamers allows modelling of an explicit sequence-fitness landscape , 2008, Nucleic acids research.

[7]  Xin Yao,et al.  Search based software testing of object-oriented containers , 2008, Inf. Sci..

[8]  Alex S. Fukunaga,et al.  Automated Discovery of Local Search Heuristics for Satisfiability Testing , 2008, Evolutionary Computation.

[9]  Ender Özcan,et al.  A comprehensive analysis of hyper-heuristics , 2008, Intell. Data Anal..

[10]  Xin Yao,et al.  A Memetic Algorithm for test data generation of Object-Oriented software , 2007, 2007 IEEE Congress on Evolutionary Computation.

[11]  Xin Yao,et al.  Estimation of distribution algorithms for testing object oriented software , 2007, 2007 IEEE Congress on Evolutionary Computation.

[12]  Joshua D. Knowles,et al.  Closed-loop, multiobjective optimization of two-dimensional gas chromatography/mass spectrometry for serum metabolomics. , 2007, Analytical chemistry.

[13]  Gregory S. Hornby,et al.  Automated Antenna Design with Evolutionary Algorithms , 2006 .

[14]  Hideyuki Takagi,et al.  User Fatigue Reduction by an Absolute Rating Data-trained Predictor in IEC , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[15]  Eckart Zitzler,et al.  Are All Objectives Necessary? On Dimensionality Reduction in Evolutionary Multiobjective Optimization , 2006, PPSN.

[16]  Evelyne Lutton,et al.  Evolution of Fractal Shapes for Artists and Designers , 2006, Int. J. Artif. Intell. Tools.

[17]  Corina S. Pasareanu,et al.  Test input generation for java containers using state matching , 2006, ISSTA '06.

[18]  Joachim Wegener,et al.  Evolutionary unit testing of object-oriented software using strongly-typed genetic programming , 2006, GECCO '06.

[19]  Aravind Srinivasan,et al.  Innovization: innovating design principles through optimization , 2006, GECCO.

[20]  David B. Fogel,et al.  The Blondie25 Chess Program Competes Against Fritz 8.0 and a Human Chess Master , 2006, 2006 IEEE Symposium on Computational Intelligence and Games.

[21]  Michael Ellims,et al.  The Economics of Unit Testing , 2006, Empirical Software Engineering.

[22]  Marco Dorigo,et al.  Cooperative hole avoidance in a swarm-bot , 2006, Robotics Auton. Syst..

[23]  Carlos A. Coello Coello,et al.  Evolutionary multi-objective optimization: a historical view of the field , 2006, IEEE Comput. Intell. Mag..

[24]  H. Handa,et al.  Robust route optimization for gritting/salting trucks: a CERCIA experience , 2006, IEEE Computational Intelligence Magazine.

[25]  A. Ravindran,et al.  Engineering Optimization: Methods and Applications , 2006 .

[26]  Michael O'Neill,et al.  Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series) , 2005 .

[27]  Colin Fyfe,et al.  Risk adjusted returns from technical trading: a genetic programming approach , 2005 .

[28]  Phil McMinn,et al.  Evolutionary testing of state-based programs , 2005, GECCO '05.

[29]  David Notkin,et al.  Symstra: A Framework for Generating Object-Oriented Unit Tests Using Symbolic Execution , 2005, TACAS.

[30]  J. S. Hunter,et al.  Statistics for Experimenters: Design, Innovation, and Discovery , 2006 .

[31]  Kalyanmoy Deb,et al.  Integrating User Preferences into Evolutionary Multi-Objective Optimization , 2005 .

[32]  Joshua D. Knowles,et al.  Closed-loop, multiobjective optimization of analytical instrumentation: gas chromatography/time-of-flight mass spectrometry of the metabolomes of human serum and of yeast fermentations. , 2005, Analytical chemistry.

[33]  D.B. Fogel,et al.  A self-learning evolutionary chess program , 2004, Proceedings of the IEEE.

[34]  Philippe Lacomme,et al.  Competitive Memetic Algorithms for Arc Routing Problems , 2004, Ann. Oper. Res..

[35]  D. Notkin,et al.  Rostra: a framework for detecting redundant object-oriented unit tests , 2004, Proceedings. 19th International Conference on Automated Software Engineering, 2004..

[36]  Paolo Tonella,et al.  Evolutionary testing of classes , 2004, ISSTA '04.

[37]  Sarfraz Khurshid,et al.  Test input generation with java PathFinder , 2004, ISSTA '04.

[38]  Phil McMinn,et al.  Hybridizing Evolutionary Testing with the Chaining Approach , 2004, GECCO.

[39]  Phil McMinn,et al.  Search‐based software test data generation: a survey , 2004, Softw. Test. Verification Reliab..

[40]  Jean-Yves Potvin,et al.  Generating trading rules on the stock markets with genetic programming , 2004, Comput. Oper. Res..

[41]  A. Messac,et al.  Normal Constraint Method with Guarantee of Even Representation of Complete Pareto Frontier , 2004 .

[42]  John J. Grefenstette,et al.  Credit assignment in rule discovery systems based on genetic algorithms , 1988, Machine Learning.

[43]  STEVEN MINTON,et al.  A reply to Zito-Wolf's book review ofLearning search control knowledge: An explanation-based approach , 2004, Machine Learning.

[44]  Phil McMinn,et al.  The State Problem for Evolutionary Testing , 2003, GECCO.

[45]  Christopher J. Neely.  Risk-adjusted, ex ante, optimal technical trading rules in equity markets , 2003 .

[46]  Graham Kendall,et al.  A Monte Carlo Hyper-Heuristic To Optimise Component Placement Sequencing For Multi Head Placement Machine , 2003 .

[47]  Graham Kendall,et al.  Hyper-Heuristics: An Emerging Direction in Modern Search Technology , 2003, Handbook of Metaheuristics.

[48]  Mukund Seshadri,et al.  GP-evolved Technical Trading Rules Can Outperform Buy and Hold , 2003 .

[49]  Mukund Seshadri,et al.  Cooperative Coevolution of Technical Trading Rules , 2003 .

[50]  P. J. Fleming,et al.  The good of the many outweighs the good of the one: evolutionary multi-objective optimization , 2003 .

[51]  Sarfraz Khurshid,et al.  TestEra A Novel Framework for Testing Java Programs y , 2003 .

[52]  Cheng Siong Lee,et al.  GP-based optimisation of technical trading indicators and profitability in FX market , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[53]  Graham Kendall,et al.  Hyperheuristics: A Robust Optimisation Method Applied to Nurse Scheduling , 2002, PPSN.

[54]  Sarfraz Khurshid,et al.  Korat: automated testing based on Java predicates , 2002, ISSTA '02.

[55]  André Baresel,et al.  Fitness Function Design To Improve Evolutionary Structural Testing , 2002, GECCO.

[56]  Peter Ross,et al.  Hyper-heuristics: Learning To Combine Simple Heuristics In Bin-packing Problems , 2002, GECCO.

[57]  Mark Harman,et al.  Improving Evolutionary Testing By Flag Removal , 2002, GECCO.

[58]  Shu-Heng Chen,et al.  Genetic Algorithms and Genetic Programming in Computational Finance , 2002 .

[59]  Lee Chapman,et al.  Sky‐view factor approximation using GPS receivers , 2002 .

[60]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[61]  Martin J. Oates,et al.  Fitness Gains and Mutation Patterns: Deriving Mutation Rates by Exploiting Landscape Data , 2002, FOGA.

[62]  David E. Goldberg,et al.  The Design of Innovation: Lessons from and for Competent Genetic Algorithms , 2002 .

[63]  P. Coveney,et al.  Combinatorial searches of inorganic materials using the ink-jet printer: science, philosophy and technology , 2001 .

[64]  David B. Fogel,et al.  Evolving an expert checkers playing program without using human expertise , 2001, IEEE Trans. Evol. Comput..

[65]  A. El-Fallah,et al.  Discovering Novel Fighter Combat Maneuvers , 2001 .

[66]  Martin J. Oates,et al.  PESA-II: region-based selection in evolutionary multiobjective optimization , 2001 .

[67]  Marney Colin Fyfe Heather Tarbert David Miller Jp,et al.  Risk Adjusted Returns to Technical Trading Rules: a Genetic Programming Approach , 2001 .

[68]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[69]  Graham Kendall,et al.  A Hyperheuristic Approach to Scheduling a Sales Summit , 2000, PATAT.

[70]  Alessandro Orso,et al.  Automated Testing of Classes , 2000, ISSTA '00.

[71]  Ingo Rechenberg,et al.  Case studies in evolutionary experimentation and computation , 2000 .

[72]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[73]  Robert E. Smith,et al.  Classifier systems in combat: two-sided learning of maneuvers for advanced fighter aircraft , 2000 .

[74]  Carlos A. Coello Coello,et al.  An updated survey of GA-based multiobjective optimization techniques , 2000, CSUR.

[75]  R. J. Gilbert,et al.  Efficient Improvement of Silage Additives by Using Genetic Algorithms , 2000, Applied and Environmental Microbiology.

[76]  David B. Fogel,et al.  Evolving neural networks to play checkers without relying on expert knowledge , 1999, IEEE Trans. Neural Networks.

[77]  David B. Fogel,et al.  Evolution, neural networks, games, and intelligence , 1999, Proc. IEEE.

[78]  H. Terashima-Marín,et al.  Evolution of Constraint Satisfaction strategies in examination timetabling , 1999 .

[79]  Colin Fyfe,et al.  Technical analysis versus market efficiency - a genetic programming approach , 1999 .

[80]  Paul J. Layzell,et al.  Analysis of unconventional evolved electronics , 1999, CACM.

[81]  T. W. E. Lau,et al.  SUPER‐HEURISTICS AND THEIR APPLICATIONS TO COMBINATORIAL PROBLEMS , 1999 .

[82]  Franklin Allen,et al.  Using genetic algorithms to find technical trading rules , 1999 .

[83]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[84]  Peter Ross,et al.  A Heuristic Combination Method for Solving Job-Shop Scheduling Problems , 1998, PPSN.

[85]  Peter Ross,et al.  Solving a Real-World Problem Using an Evolving Heuristically Driven Schedule Builder , 1998, Evolutionary Computation.

[86]  Peter Nordin,et al.  Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications , 1998 .

[87]  Peter J. Fleming,et al.  Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[88]  Peter Ross,et al.  Some Observations about GA-Based Exam Timetabling , 1997, PATAT.

[89]  Bart Selman,et al.  Evidence for Invariants in Local Search , 1997, AAAI/IAAI.

[90]  Jonathan Schaeffer,et al.  One jump ahead - challenging human supremacy in checkers , 1997, J. Int. Comput. Games Assoc..

[91]  Shigenobu Kobayashi,et al.  Edge Assembly Crossover: A High-Power Genetic Algorithm for the Travelling Salesman Problem , 1997, ICGA.

[92]  Peter J. Angeline,et al.  Genetic programming's continued evolution , 1996 .

[93]  Jonathan Schaeffer,et al.  CHINOOK: The World Man-Machine Checkers Champion , 1996, AI Mag..

[94]  J. Thornes,et al.  A COMPARISON BETWEEN SPATIAL WINTER INDICES AND EXPENDITURE ON WINTER ROAD MAINTENANCE , 1996 .

[95]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[96]  Shu-Heng Chen,et al.  Toward a computable approach to the efficient market hypothesis: An application of genetic programming , 1995 .

[97]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[98]  Kalyanmoy Deb,et al.  Real-coded Genetic Algorithms with Simulated Binary Crossover: Studies on Multimodal and Multiobjective Problems , 1995, Complex Syst..

[99]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[100]  Bart Selman,et al.  Noise Strategies for Improving Local Search , 1994, AAAI.

[101]  Peter Ross,et al.  A Promising Hybrid GA/Heuristic Approach for Open-Shop Scheduling Problems , 1994, ECAI.

[102]  S. N. Kramer,et al.  An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and Its Applications to Mechanical Design , 1994 .

[103]  Phyllis G. Frankl,et al.  The ASTOOT approach to testing object-oriented programs , 1994, TSEM.

[104]  Toby Walsh,et al.  Towards an Understanding of Hill-Climbing Procedures for SAT , 1993, AAAI.

[105]  Gerald DeJong,et al.  Learning Search Control Knowledge for Deep Space Network Scheduling , 1993, ICML.

[106]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[107]  Hector J. Levesque,et al.  A New Method for Solving Hard Satisfiability Problems , 1992, AAAI.

[108]  Karl Sims,et al.  Artificial evolution for computer graphics , 1991, SIGGRAPH.

[109]  L. Gold,et al.  Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase. , 1990, Science.

[110]  Bogdan Korel,et al.  Automated Software Test Data Generation , 1990, IEEE Trans. Software Eng..

[111]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[112]  H. Chernoff.  Sequential Analysis and Optimal Design , 1987 .

[113]  John H. Holland,et al.  Induction: Processes of Inference, Learning, and Discovery , 1987, IEEE Expert.

[114]  Robert L. Shaw,et al.  Fighter Combat: Tactics and Maneuvering , 1985 .

[115]  Boris Beizer,et al.  Software Testing Techniques , 1983 .

[116]  Glenford J. Myers,et al.  Art of Software Testing , 1979 .

[117]  James C. King,et al.  Symbolic execution and program testing , 1976, CACM.

[118]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[119]  G. Thompson,et al.  Algorithms for Solving Production-Scheduling Problems , 1960 .

[120]  Arthur L. Samuel,et al.  Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..

[121]  George E. P. Box,et al.  Evolutionary Operation: a Method for Increasing Industrial Productivity , 1957 .

[122]  R Fisher,et al.  Design of Experiments , 1936 .