| Author | Title | Year | Journal/Proceedings | Reftype | DOI/URL |
|---|---|---|---|---|---|
| Miller, J. F. & Banzhaf, W. | Evolving the program for a cell: from French flags to Boolean circuits | 2003 | On Growth, Form and Computers | incollection | URL |
| 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. |
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BibTeX:
@incollection{KumarBentley2003,
author = {Julian F. Miller and Wolfgang Banzhaf},
title = {Evolving the program for a cell: from French flags to Boolean circuits},
booktitle = {On Growth, Form and Computers},
publisher = {Academic Press},
year = {2003},
url = {http://web.cs.mun.ca/~banzhaf/papers/chapter_finalrevision.pdf}
}
|
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| O'Neill, M. & Ryan, C. | Grammatical Evolution | 2001 | IEEE Transactions on Evolutionary Computation | article | DOI |
| 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 | |||||
BibTeX:
@article{oneill:2001:TEC,
author = {Michael O'Neill and Conor Ryan},
title = {Grammatical Evolution},
journal = {IEEE Transactions on Evolutionary Computation},
year = {2001},
volume = {5},
number = {4},
pages = {349--358},
doi = {doi:10.1109/4235.942529}
}
|
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| Rowland, J. | On Genetic Programming and Knowledge Discovery in Transcriptome Data | 2004 | Proceedings of the 2004 IEEE Congress on Evolutionary Computation | inproceedings | |
| 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. | |||||
BibTeX:
@inproceedings{rowland:2004:ogpakditd,
author = {Jem Rowland},
title = {On Genetic Programming and Knowledge Discovery in Transcriptome Data},
booktitle = {Proceedings of the 2004 IEEE Congress on Evolutionary Computation},
publisher = {IEEE Press},
year = {2004},
pages = {158--165}
}
|
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| Rylander, B. | Computational complexity and the genetic algorithm | 2001 | School: University of Idaho | phdthesis | |
| 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. | |||||
BibTeX:
@phdthesis{oai:xtcat.oclc.org:OCLCNo/ocm48450722,
author = {Bart Rylander},
title = {Computational complexity and the genetic algorithm},
school = {University of Idaho},
year = {2001}
}
|
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| Rylander, B., Soule, T. & Foster, J. | Computational Complexity, Genetic Programming, and Implications | 2001 | Genetic Programming, Proceedings of EuroGP'2001 | inproceedings | URL |
| 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. | |||||
BibTeX:
@inproceedings{rylander:2001:EuroGP,
author = {Bart Rylander and Terry Soule and James Foster},
title = {Computational Complexity, Genetic Programming, and Implications},
booktitle = {Genetic Programming, Proceedings of EuroGP'2001},
publisher = {Springer-Verlag},
year = {2001},
volume = {2038},
pages = {348--360},
url = {http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=348}
}
|
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| Streeter, M. J., Keane, M. A. & Koza, J. R. | Iterative Refinement Of Computational Circuits Using Genetic Programming [BibTeX] |
2002 | GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference | inproceedings | URL |
BibTeX:
@inproceedings{streeter:2002:gecco,
author = {Matthew J. Streeter and Martin A. Keane and John R. Koza},
title = {Iterative Refinement Of Computational Circuits Using Genetic Programming},
booktitle = {GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference},
publisher = {Morgan Kaufmann Publishers},
year = {2002},
pages = {877--884},
url = {http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf}
}
|
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| Takagi, H. | Interactive Evolutionary Computation: Fusion of the Capabilities of EC Optimization and Human Evaluation | 2001 | Proceedings of the IEEE | article | |
| 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 | |||||
BibTeX:
@article{takagi:2001:ieee,
author = {Hideyuki Takagi},
title = {Interactive Evolutionary Computation: Fusion of the Capabilities of {EC} Optimization and Human Evaluation},
journal = {Proceedings of the IEEE},
year = {2001},
volume = {89},
number = {9},
pages = {1275--1296},
note = {Invited Paper}
}
|
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| Vitanyi, P. | A discipline of evolutionary programming | 2000 | Theoretical Computer Science | article | URL |
| 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. | |||||
BibTeX:
@article{Vitanyi:2000:DEP,
author = {Paul Vitanyi},
title = {A discipline of evolutionary programming},
journal = {Theoretical Computer Science},
year = {2000},
volume = {241},
number = {1--2},
pages = {3--23},
url = {http://www.elsevier.nl/gej-ng/10/41/16/175/21/22/article.pdf}
}
|
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| Walker, J. A. & Miller, J. F. | Investigating the performance of module acquisition in cartesian genetic programming [BibTeX] |
2005 | GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation | inproceedings | URL |
BibTeX:
@inproceedings{1068287,
author = {James Alfred Walker and Julian Francis Miller},
title = {Investigating the performance of module acquisition in cartesian genetic programming},
booktitle = {{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation},
publisher = {ACM Press},
year = {2005},
volume = {2},
pages = {1649--1656},
url = {http://doi.acm.org/10.1145/1068009.1068287}
}
|
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| Genetic Programming, Proceedings of EuroGP'2001 [BibTeX] |
2001 | proceedings | URL | ||
BibTeX:
@proceedings{miller:2001:gp,,
title = {Genetic Programming, Proceedings of Euro{GP}'2001},
publisher = {Springer-Verlag},
year = {2001},
volume = {2038},
url = {http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038}
}
|
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