@proceedings{miller:2001:gp, title = {Genetic Programming, Proceedings of Euro{GP}'2001}, address = {Lake Como, Italy}, editor = {Julian Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon}, month = {18-20 April}, publisher = {Springer-Verlag}, series = {LNCS}, volume = 2038, year = 2001, url = {http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038}, address = {Berlin}, isbn = {3-540-41899-7}, organisation = {EvoNET}, notes = {EuroGP'2001}, size = {391 pages approx}, biburl = {http://www.bibsonomy.org/bibtex/2c6b0cf2cb5e153bca4b1dc0f9e64e61f/brazovayeye}, keywords = {Complexity, Evolution, Arm, individuals, modelling, Neutral VLSI Character Parallel VHDL, Bloat, structures, DNA, Cellular Intrinsic representations, genetic robot, Dynamic Knowledge prediction, One-then-zeros distributions, Retina, Series Recognition, CAD, bias, Causality, Computational Linear Machine MAX Systems, design, Fixed Length Layered Program Evolvable size, Time model, Problem, Developmental Hardware, Polymorphism, Problem points, BDD, Multi-expression Mapping, Symbolic Process Extraction, Artificial Learning, function robust, problem, Inverse Crossover Controller Evolvability, Block-oriented Genetic algorithms, control, Turing landscape, Image Pattern Contour Discovery, Feature Generator, STGP, detection, Boolean Evolution representation, Reasoning, Kinematics, Regression, programming, Genotype-Phenotype modular Grammatical machines, processing, of Robotic Multipopulation Iterated Function Animat, mutation, Active distributed Fitness, Trees,} } @incollection{KumarBentley2003, title = {Evolving the program for a cell: from French flags to Boolean circuits}, author = {Julian F. Miller and Wolfgang Banzhaf}, booktitle = {On Growth, Form and Computers}, editor = {Sanjeev Kumar and Peter J. Bentley}, month = {October}, publisher = {Academic Press}, year = 2003, url = {http://web.cs.mun.ca/~banzhaf/papers/chapter_finalrevision.pdf}, isbn = {0-12-428765-4}, size = {33 pages}, abstract = {Introduction The development of an entire organism from a single cell is one of the most profound and awe inspiring phenomena in the whole of the natural world. The complexity of living systems itself dwarfs anything that man has produced. This is all the more the case for the processes that lead to these intricate systems. In each phase of the development of a multi-cellular being, this living system has to survive, whether stand-alone or supported by various structures and processes provided by other living systems. Organisms construct themselves, out of humble single-celled beginnings, riding waves of interaction between the information residing in their genomes inherited from the evolutionary past of their species via their progenitors and the resources of their environment.}, biburl = {http://www.bibsonomy.org/bibtex/2de24cd18bdde0fd145f7845e0fef8255/brazovayeye}, keywords = {genetic programming, computational algorithms, artificial life development,} } @article{oneill:2001:TEC, title = {Grammatical Evolution}, author = {Michael O'Neill and Conor Ryan}, journal = {IEEE Transactions on Evolutionary Computation}, month = {August}, number = 4, pages = {349--358}, volume = 5, year = 2001, issn = {1089-778X}, notes = {URL broken 7 June 2003}, doi = {doi:10.1109/4235.942529}, size = {10 pages}, abstract = {We present grammatical evolution, an evolutionary algorithm that can evolve complete programs in an arbitrary language using a variable-length binary string. The binary genome determines which production rules in a Backus-Naur form grammar definition are used in a genotype-to-phenotype mapping process to a program. We demonstrate how expressions and programs of arbitrary complexity may be evolved and compare its performance to genetic programming}, biburl = {http://www.bibsonomy.org/bibtex/263ddb74adb60bcfb7875d5d5cdcc799d/brazovayeye}, keywords = {Genome, evolution, automatic Crossover, Mapping, complexity computational Grammar, genetic Linear grammatical Genotype-Phenotype programming, algorithms,} } @inproceedings{rowland:2004:ogpakditd, title = {On Genetic Programming and Knowledge Discovery in Transcriptome Data}, address = {Portland, Oregon}, author = {Jem Rowland}, booktitle = {Proceedings of the 2004 IEEE Congress on Evolutionary Computation}, month = {20-23 June}, pages = {158--165}, publisher = {IEEE Press}, year = 2004, isbn = {0-7803-8515-2}, abstract = {This paper concerns the use of Genetic Programming (GP) for supervised classification of transcriptome (gene expression) data. In such applications GP can produce accurate predictive models that generalize well and use only very few gene expression values. It is often suggested that the selected genes are therefore of biological significance in discriminating the classes. The paper presents a preliminary study of successful parsimonious GP models to investigate the extent to which the selected variables contribute to the classification. The work is based on a readily available and well studied dataset that represents gene expression values for two groups of patients with different forms of Leukaemia.}, biburl = {http://www.bibsonomy.org/bibtex/27d52d9454ee28d82eb1fe876b70a545d/brazovayeye}, keywords = {Biology genetic and Computation Computational Bioinformatics programming, algorithms, in Evolutionary} } @inproceedings{rylander:2001:EuroGP, title = {Computational Complexity, Genetic Programming, and Implications}, address = {Lake Como, Italy}, author = {Bart Rylander and Terry Soule and James Foster}, booktitle = {Genetic Programming, Proceedings of EuroGP'2001}, editor = {Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon}, month = {18-20 April}, pages = {348--360}, publisher = {Springer-Verlag}, series = {LNCS}, volume = 2038, year = 2001, url = {http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=348}, address = {Berlin}, isbn = {3-540-41899-7}, organisation = {EvoNET}, notes = {EuroGP'2001, part of \cite{miller:2001:gp}, size = {13 pages}, abstract = {Recent theory work has shown that a Genetic Program (GP) used to produce programs may have output that is bounded above by the GP itself [l]. This paper presents proofs that show that 1) a program that is the output of a GP or any inductive process has complexity that can be bounded by the Kolmogorov complexity of the originating program; 2) this result does not hold if the random number generator used in the evolution is a true random source; and 3) an optimization problem being solved with a GP will have a complexity that can be bounded below by the growth rate of the minimum length problem representation used for the implementation. These results are then used to provide guidance for GP implementation.}, biburl = {http://www.bibsonomy.org/bibtex/2ee3e3aac936529058a7c8e31d21b0d85/brazovayeye}, keywords = {algorithms, programming, Complexity, Computational genetic Quantum Computing} } @phdthesis{oai:xtcat.oclc.org:OCLCNo/ocm48450722, title = {Computational complexity and the genetic algorithm}, author = {Bart Rylander}, month = {November}, school = {University of Idaho}, year = 2001, oai = {oai:xtcat.oclc.org:OCLCNo/ocm48450722}, size = {pages}, description = {Thesis (Ph. D.)--University of Idaho, 2001.; Includes bibliographical references (leaves 104-108).}, abstract = {Includes the text of four previously published papers: Computational complexity and genetic algorithms -- Genetic algorithms and hardness -- Computational complexity, genetic programming, and implications -- Quantum evolutionary programming.}, biburl = {http://www.bibsonomy.org/bibtex/28c395acbe40644102eb05adabea703e8/brazovayeye}, keywords = {complexity, computational Quantum algorithms, genetic programming programming, evolutionary} } @inproceedings{streeter:2002:gecco, title = {Iterative Refinement Of Computational Circuits Using Genetic Programming}, address = {New York}, author = {Matthew J. Streeter and Martin A. Keane and John R. Koza}, booktitle = {GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference}, editor = {W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska}, month = {9-13 July}, pages = {877--884}, publisher = {Morgan Kaufmann Publishers}, year = 2002, url = {http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf}, address = {San Francisco, CA 94104, USA}, isbn = {1-55860-878-8}, biburl = {http://www.bibsonomy.org/bibtex/265729a5488c6b3bbfaaacb09f3fac431/brazovayeye}, keywords = {correction, approximation algorithms, programming, circuits, iterative numerical error computational refinement, genetic} } @article{takagi:2001:ieee, title = {Interactive Evolutionary Computation: Fusion of the Capabilities of {EC} Optimization and Human Evaluation}, author = {Hideyuki Takagi}, journal = {Proceedings of the IEEE}, month = {September}, note = {Invited Paper}, number = 9, pages = {1275--1296}, volume = 89, year = 2001, issn = {0018-9219}, notes = {CODEN: IEEPAD Inspec Accession Number: 7053972}, size = {22 pages}, abstract = {We survey the research on interactive evolutionary computation (IEC). The IEC is an EC that optimises systems based on subjective human evaluation. The definition and features of the IEC are first described and then followed by an overview of the IEC research. The overview primarily consists of application research and interface research. In this survey the IEC application fields include graphic arts and animation, 3D computer graphics lighting, music, editorial design, industrial design, facial image generation, speed processing and synthesis, hearing aid fitting, virtual reality, media database retrieval, data mining, image processing, control and robotics, food industry, geophysics, education, entertainment, social system, and so on. The interface research to reduce human fatigue is also included. Finally, we discuss the IEC from the point of the future research direction of computational intelligence. This paper features a survey of about 250 IEC research papers}, biburl = {http://www.bibsonomy.org/bibtex/224083bc53650b29a7f09aad1ff14bc93/brazovayeye}, keywords = {animation, robots, human graphic computational mining, computation, genetic user factors, graphics, data robotics, interfaces, interactive computer arts, algorithms, interface intelligence, systems, programming, evolutionary} } @article{Vitanyi:2000:DEP, title = {A discipline of evolutionary programming}, author = {Paul Vitanyi}, journal = {Theoretical Computer Science}, month = {28 June}, number = {1--2}, pages = {3--23}, volume = 241, year = 2000, url = {http://www.elsevier.nl/gej-ng/10/41/16/175/21/22/article.pdf}, bibdate = {Tue Oct 31 11:38:29 MST 2000}, issn = {0304-3975}, notes = {Update of \cite{alt96*67}, coden = {TCSCDI}, size = {21 pages}, abstract = {Genetic fitness optimization using small populations or small population updates across generations generally suffers from randomly diverging evolutions. We propose a notion of highly probable fitness optimization through feasible evolutionary computing runs on small size populations. Based on rapidly mixing Markov chains, the approach pertains to most types of evolutionary genetic algorithms, genetic programming and the like. We establish that for systems having associated rapidly mixing Markov chains and appropriate stationary distributions the new method finds optimal programs (individuals) with probability almost 1. To make the method useful would require a structured design methodology where the development of the program and the guarantee of the rapidly mixing property go hand in hand. We analyze a simple example to show that the method is implementable. More significant examples require theoretical advances, for example with respect to the Metropolis filter.}, biburl = {http://www.bibsonomy.org/bibtex/23c6e75e2c0433cd977a2a9c60c559af4/brazovayeye}, keywords = {Data Intelligence, programming, Learning, algorithms, Algorithms, Complexity, and genetic Artificial Computing, Multiagent Structures Computational Systems Neural Evolutionary} } @inproceedings{1068287, title = {Investigating the performance of module acquisition in cartesian genetic programming}, address = {Washington DC, USA}, author = {James Alfred Walker and Julian Francis Miller}, booktitle = {{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation}, editor = {Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler}, month = {25-29 June}, pages = {1649--1656}, publisher = {ACM Press}, volume = 2, year = 2005, url = {http://doi.acm.org/10.1145/1068009.1068287}, address = {New York, NY, 10286-1405, USA}, isbn = {1-59593-010-8}, organisation = {ACM SIGEVO (formerly ISGEC)}, biburl = {http://www.bibsonomy.org/bibtex/24c41d66d6d01443cc2dde71aec44b735/brazovayeye}, keywords = {module programming, comparators, computational acquisition, performance digital algorithms, adders, cartesian modularity, multipliers, design, genetic effort,} } @inproceedings{aler:2001:glckg, title = {Grammars for Learning Control Knowledge with {GP}}, address = {COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea}, author = {Ricardo Aler and Daniel Borrajo and Pedro Isasi}, booktitle = {Proceedings of the 2001 Congress on Evolutionary Computation CEC2001}, month = {27-30 May}, pages = {1220--1227}, publisher = {IEEE Press}, year = 2001, url = {http://scalab.uc3m.es/~dborrajo/papers/cec01.ps.gz}, isbn = {0-7803-6658-1}, size = {8 pages}, abstract = {In standard GP there are no constraints on the structure to evolve: any combination of functions and terminals is valid. However, sometimes GP is used to evolve structures that must respect some constraints. Instead of ad-hoc mechanisms, grammars can be used to guarantee that individuals comply with the language restrictions. In addition, grammars permit great flexibility to define the search space. EVOCK (Evolution of Control Knowledge) is a GP based system that learns control rules for PRODIGY, an AI planning system. EVOCK uses a grammar to constrain individuals to PRODIGY 4.0 control rule syntax. The authors describe the grammar specific details of EVOCK. Also, the grammar approach flexibility has been used to extend the control rule language used by EVOCK in earlier work. Using this flexibility, tests were performed to determine whether using combinations of several types of control rules for planning was better than using only the standard select type. Experiments have been carried out in the blocksworld domain that show that using the combination of types of control rules does not get better individuals, but it produces good individuals more frequently}, biburl = {http://www.bibsonomy.org/bibtex/258273cc28391b14cd9575da2ae479dcd/brazovayeye}, keywords = {algorithms, domain, Control control intelligence), standard of search programming, rule mechanisms, linguistics, system, learning planning approach rules, restrictions, Knowledge, ad-hoc specific, problems, based blocksworld language, syntax, flexibility, (artificial GP language space, grammar knowledge Evolution GP, computational genetic select grammars, learning, PRODIGY, AI type EVOCK,} } @article{Archetti:2007:GPEM, title = {Genetic programming for computational pharmacokinetics in drug discovery and development}, author = {Francesco Archetti and Stefano Lanzeni and Enza Messina and Leonardo Vanneschi}, journal = {Genetic Programming and Evolvable Machines}, month = {December}, note = {special issue on medical applications of Genetic and Evolutionary Computation}, number = 4, pages = {413--432}, volume = 8, year = 2007, issn = {1389-2576}, doi = {doi:10.1007/s10710-007-9040-z}, abstract = {The success of a drug treatment is strongly correlated with the ability of a molecule to reach its target in the patient's organism without inducing toxic effects. Moreover the reduction of cost and time associated with drug discovery and development is becoming a crucial requirement for pharmaceutical industry. Therefore computational methods allowing reliable predictions of newly synthesised compounds properties are of outmost relevance. In this paper we discuss the role of genetic programming in predictive pharmacokinetics, considering the estimation of adsorption, distribution, metabolism, excretion and toxicity processes (ADMET) that a drug undergoes into the patient's organism. We compare genetic programming with other well known machine learning techniques according to their ability to predict oral bioavailability (%F), median oral lethal dose (LD50) and plasma-protein binding levels (%PPB). Since these parameters respectively characterise the percentage of initial drug dose that effectively reaches the systemic blood circulation, the harmful effects and the distribution into the organism of a drug, they are essential for the selection of potentially good molecules. Our results suggest that genetic programming is a valuable technique for predicting pharmacokinetics parameters, both from the point of view of the accuracy and of the generalisation ability.}, biburl = {http://www.bibsonomy.org/bibtex/2758aa69c3921998e0710fe35dab7131f/brazovayeye}, keywords = {genetic Drug QSAR Computational pharmacokinetics, discovery, programming, algorithms,} } @article{Azad:2004:ASC, title = {An evolutionary approach to Wall Sheer Stress prediction in a grafted artery}, author = {R. Muhammad Atif Azad and Ali R. Ansari and Conor Ryan and Michael Walsh and Tim McGloughlin}, journal = {Applied Soft Computing}, month = {May}, number = 2, pages = {139--148}, publisher = {Elsevier}, volume = 4, year = 2004, issn = {1568-4946}, notes = {http://www.elsevier.com/wps/find/journaldescription.cws_home/621920/description#description}, doi = {doi:10.1016/j.asoc.2003.11.001}, abstract = {Restoring the blood supply to a diseased artery is achieved by using a vascular bypass graft. The surgical procedure is a well documented and successful technique. The most commonly cited hemodynamic factor implicated in the disease initiation and proliferation processes at graft/artery junctions is Wall Shear Stress (WSS). WSS distributions are predicted using numerical simulations as they can provide quick and precise results to assess the effects that alternative graft/artery junction geometries have on the WSS distributions in bypass grafts. Validation of the numerical model is required and in vitro studies, using laser Doppler anemometry (LDA), have been employed to achieve this. Numerically, the Wall Shear Stress is predicted using velocity values stored in the computational cell near the wall and assuming zero velocity at the wall. Experimentally obtained velocities require a mathematical model to describe their behavior. This study employs a grammar based evolutionary algorithm termed Chorus for this purpose and demonstrates that Chorus successfully attains this objective. It is shown that even with the lack of domain knowledge, the results produced by this automated system are comparable to the results in the literature.}, biburl = {http://www.bibsonomy.org/bibtex/28448ed00ed946faf426e3f99c0751aac/brazovayeye}, keywords = {algorithms, evolution, modeling, anemometry, programming, Wall Fluid Stress, genetic Computational Doppler Dynamics chorus Shear Mathematical grammatical system, Laser} } @book{Brabazon:2008:edbook, title = {Natural Computing in Computational Finance}, editor = {Anthony Brabazon and Michael O'Neill}, month = {April}, note = {Due 27 Feb 2008}, publisher = {Springer}, series = {Studies in Computational Intelligence}, volume = 100, year = 2008, url = {http://www.springer.com/engineering/book/978-3-540-77476-1}, size = {approx 300 pages}, abstract = {Natural Computing in Computational Finance is a innovative volume containing fifteen chapters which illustrate cutting-edge applications of natural computing or agent-based modelling in modern computational finance. Following an introductory chapter the book is organised into three sections. The first section deals with optimisation applications of natural computing demonstrating the application of a broad range of algorithms including, genetic algorithms, differential evolution, evolution strategies, quantum-inspired evolutionary algorithms and bacterial foraging algorithms to multiple financial applications including portfolio optimization, fund allocation and asset pricing. The second section explores the use of natural computing methodologies such as genetic programming, neural network hybrids and fuzzy-evolutionary hybrids for model induction in order to construct market trading, credit scoring and market prediction systems. The final section illustrates a range of agent-based applications including the modeling of payment card and financial markets. Each chapter provides an introduction to the relevant natural computing methodology as well as providing a clear description of the financial application addressed. The book was written to be accessible to a wide audience and should be of interest to practitioners, academics and students, in the fields of both natural computing and finance.}, biburl = {http://www.bibsonomy.org/bibtex/2bcfd14824ab75ab98c2f7e0efa1b78bc/brazovayeye}, keywords = {genetic strategies, algorithms finance, programming, quantum-inspired bacterial computational algorithms, evolutionary foraging, evolution evolution, differential} } @inproceedings{SHChen:1999:gpabmsm, title = {Genetic Programming in the Agent-Based Modeling of Stock Markets}, address = {Boston College, MA, USA}, author = {Shu-Heng Chen and Chia-Hsuan Yeh}, booktitle = {Fifth International Conference: Computing in Economics and Finance}, editor = {David A. Belsley and Christopher F. Baum}, month = {24-26 June}, note = {Book of Abstracts}, pages = 77, year = 1999, url = {http://fmwww.bc.edu/cef99/papers/ChenYeh.pdf}, size = {22 pages}, abstract = {In this paper, we propose a new architecture to study artificial stock markets. This architecture rests on a mechanism called school which is a procedure to map the phenotype to the genotype or, in plain English, to uncover the secret of success. We propose an agent-based model of school, and consider school as an evolving population driven by single-population GP (SGP). The architecture also takes into consideration traders' search behavior. By simulated annealing, traders' search density can be connected to psychological factors, such as peer pressure or economic factors such as the standard of living. This market architecture was then implemented in a standard artificial stock market. Our econometric study of the resultant artificial time series evidences that the return series is independently and identically distributed (iid), and hence supports the efficient market hypothesis (EMH). What is interesting though is that this iid series was generated by traders, who do not believe in the EMH at all. In fact, our study indicates that many of our traders were able to find useful signals quite often from business school, even though these signals were short-lived.}, biburl = {http://www.bibsonomy.org/bibtex/208e8cd2120283bd58ba8630a493016d4/brazovayeye}, keywords = {Learning, Agent-Based Markets, Computational Artificial programming, Stock Peer Pressure genetic Business algorithms, Economics, School, Annealing, Social Simulated} } @article{chen:1999:SC, title = {Modeling the expectations of inflation in the {OLG} model with genetic programming}, author = {Shu-Heng Chen and Chia-Hsuan Yeh}, journal = {Soft Computing - A Fusion of Foundations, Methodologies and Applications}, month = {September}, number = 3, pages = {53--62}, volume = 3, year = 1999, issn = {1432-7643}, doi = {doi:10.1007/s005000050053}, abstract = {genetic programming (GP) is employed to model learning and adaptation in the overlapping generations model, one of the most popular dynamic economic models. Using a model of inflation with multiple equilibria as an illustrative example, we show that our GP-based agents are able to coordinate their actions to achieve the Pareto-superior equilibrium (the low-inflation steady state) rather than the Pareto inferior equilibrium (the high-inflation steady state). We also test the robustness of this result with different initial conditions, economic parameters, GP control parameters, and the selection mechanism. We find that as long as the survival-of-the-fittest principle is maintained, the evolutionary operators are only secondarily important. However, once the survival-of-the-fittest principle is absent, the well-coordinated economy is also gone and the inflation rate can jump quite wildly. To some extent, these results shed light on the biological foundations of economics.}, biburl = {http://www.bibsonomy.org/bibtex/20499b1e81f021db32557f80e20ff0c40/brazovayeye}, keywords = {algorithms, genetic generations rationality, economics, bounded equilibrium models, agent-based computational Pareto-superior programming, overlapping} } @article{Shu-HengChen:2001:JEDC, title = {Evolving traders and the business school with genetic programming: {A} new architecture of the agent-based artificial stock market}, author = {Shu-Heng Chen and Chia-Hsuan Yeh}, journal = {Journal of Economic Dynamics and Control}, month = {March}, number = {3-4}, pages = {363--393}, volume = 25, year = 2001, doi = {doi:10.1016/S0165-1889(00)00030-0}, abstract = {we propose a new architecture to study artificial stock markets. This architecture rests on a mechanism called `school' which is a procedure to map the phenotype to the genotype or, in plain English, to uncover the secret of success. We propose an agent-based model of `school', and consider school as an evolving population driven by single-population GP (SGP). The architecture also takes into consideration traders' search behavior. By simulated annealing, traders' search density can be connected to psychological factors, such as peer pressure or economic factors such as the standard of living. This market architecture was then implemented in a standard artificial stock market. Our econometric study of the resultant artificial time series evidences that the return series is independently and identically distributed (iid), and hence supports the efficient market hypothesis (EMH). What is interesting though is that this iid series was generated by traders, who do not believe in the EMH at all. In fact, our study indicates that many of our traders were able to find useful signals quite often from business school, even though these signals were short-lived.}, biburl = {http://www.bibsonomy.org/bibtex/2617211268357a6e54c82b82e46f66294/brazovayeye}, keywords = {markets Agent-based Social programming, economics, Artificial genetic Business computational stock algorithms, school, learning,} } @incollection{ChenLiao:2002:gagpcf, title = {Price Discovery in Agent-Based Computational Modeling of the Artificial Stock Market}, author = {Shu-Heng Chen and Chung-Chih Liao}, booktitle = {Genetic Algorithms and Genetic Programming in Computational Finance}, chapter = 16, editor = {Shu-Heng Chen}, pages = {335--356?}, publisher = {Kluwer Academic Press}, year = 2002, url = {http://www.aiecon.org/staff/shc/pdf/apga002.pdf}, isbn = {0-7923-7601-3}, notes = {part of \cite{chen:2002:gagpcf}, size = {8 pages}, abstract = {the behaviour of price discovery within a context of an agent based stock market, in which the twin assumptions, namely, rational expectations and the representative agents normally made in mainstream economics, are removed. In this model, traders stochastically update their forecasts by searching the business school whose evolution is driven by genetic programming. Via these agent based simulations, it is found that, except for some extreme cases, the mean prices generated from these artificial markets deviate from the homogeneous rational expectation equilibrium (HREE) prices no more than by 20per cent. This figure provides us a rough idea on how different we can possibly be when the twin assumptions are not taken. Furthermore, while the HREE price should be a deterministic constant in all of our simulations, the artificial price series generated exhibit quite wild fluctuation, which may be coined as the well-known excessive volatility in finance.}, biburl = {http://www.bibsonomy.org/bibtex/242ca0ce994103f97e50392fe4c7c851e/brazovayeye}, keywords = {Volatility Computational Price programming, Finance, genetic Excessive Expectation Homogeneous algorithms, Discovery, Rational Agent-Based Equilibrium,} } @article{Shu-HengChen:2004:IJMPB, title = {Functional Modularity in the Fundamentals of Economic Theory: Toward an Agent-Based Economic Modeling of the Evolution of Technology}, author = {Shu-Heng Chen and Bin-Tzong Chie}, journal = {International Journal of Modern Physics B}, month = {July 30}, number = {17-19}, pages = {2376--2386}, volume = 18, year = 2004, doi = {doi:10.1142/S0217979204025403}, abstract = {No matter how commonly the term innovation has been used in economics, a concrete analytical or computational model of innovation is not yet available. This paper argues that a breakthrough can be made with genetic programming, and proposes a functional-modularity approach to an agent-based computational economic model of innovation.}, biburl = {http://www.bibsonomy.org/bibtex/244e1ed53af685219a476f5eb82a803fa/brazovayeye}, keywords = {Agent-based computational genetic economics, algorithms, innovation, functional modularity programming,} } @incollection{Chen:2006:CNEI, title = {A Functional Modularity Approach to Agent-based Modeling of the Evolution of Technology}, author = {Shu-Heng Chen and Bin-Tzong Chie}, booktitle = {The Complex Networks of Economic Interactions: Essays in Agent-Based Economics and Econophysics}, editor = {Akira Namatame and Yuuji Aruka and Taisei Kaizouji}, month = {January}, pages = {165--178}, publisher = {Springer}, series = {Lecture Notes in Economics and Mathematical Systems}, volume = 567, year = 2006, isbn = {3-540-28726-4}, abstract = {No matter how commonly the term innovation has been used in economics, a concrete analytical or computational model of innovation is not yet available. This paper argues that a breakthrough can be made with genetic programming, and proposes a functional-modularity approach to an agent-based computational economic model of innovation.}, biburl = {http://www.bibsonomy.org/bibtex/24bbcc1b29d0421d2a873393ec009a304/brazovayeye}, keywords = {economics, innovation, agent-based genetic functional computational modularity programming, algorithms,} }