The genotypic complexity of evolved fault-tolerant and noise-robust circuits

Hartmann, Morten; Haddow, Pauline C.; Lehre, Per Kristian

doi:10.1016/j.biosystems.2006.09.017

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…According to Hartmann, Haddow, and Lehre (2007), for instance, so-called ''artificial development'' is used to solve complex computational problems by way of replacing direct genetic encoding with indirect ''developmental encoding.'' Developmental encoding is a way to build electronic circuits by adapting two processes that are common in biological developmental systems (Roggen, Federici, & Floreano, 2007): the computation proceeds by using a signaling phase, where information is communicated locally within a given circuit (just as cells in an organism rely on internal monitoring to maintain homeostasis and functionality in response to their immediate surroundings); this is complemented by an expression phase, where local components (''cells'') of a circuit adopt a particular functional state depending on the signal that they have received in the previous phase (the rough equivalent of adaptive cell memory in biological organisms).…”

Section: Developmental Encoding and Software Engineeringmentioning

confidence: 99%

The mismeasure of machine: Synthetic biology and the trouble with engineering metaphors

Boudry

Pigliucci

2013

Studies in History and Philosophy of Science Part C: Studies in

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a b s t r a c tThe scientific study of living organisms is permeated by machine and design metaphors. Genes are thought of as the ''blueprint'' of an organism, organisms are ''reverse engineered'' to discover their functionality, and living cells are compared to biochemical factories, complete with assembly lines, transport systems, messenger circuits, etc. Although the notion of design is indispensable to think about adaptations, and engineering analogies have considerable heuristic value (e.g., optimality assumptions), we argue they are limited in several important respects. In particular, the analogy with human-made machines falters when we move down to the level of molecular biology and genetics. Living organisms are far more messy and less transparent than human-made machines. Notoriously, evolution is an opportunistic tinkerer, blindly stumbling on ''designs'' that no sensible engineer would come up with. Despite impressive technological innovation, the prospect of artificially designing new life forms from scratch has proven more difficult than the superficial analogy with ''programming'' the right ''software'' would suggest. The idea of applying straightforward engineering approaches to living systems and their genomesisolating functional components, designing new parts from scratch, recombining and assembling them into novel life forms-pushes the analogy with human artifacts beyond its limits. In the absence of a one-to-one correspondence between genotype and phenotype, there is no straightforward way to implement novel biological functions and design new life forms. Both the developmental complexity of gene expression and the multifarious interactions of genes and environments are serious obstacles for ''engineering'' a particular phenotype. The problem of reverse-engineering a desired phenotype to its genetic ''instructions'' is probably intractable for any but the most simple phenotypes. Recent developments in the field of bio-engineering and synthetic biology reflect these limitations. Instead of genetically engineering a desired trait from scratch, as the machine/engineering metaphor promises, researchers are making greater strides by co-opting natural selection to ''search'' for a suitable genotype, or by borrowing and recombining genetic material from extant life forms.Ó 2013 Elsevier Ltd. All rights reserved. When citing this paper, please use the full journal title Studies in History and Philosophy of Biological and Biomedical SciencesWe improve our favourite plants and animals-and how few they are-gradually by selective breeding; now a new and better peach, now a seedless grape, now a sweeter and larger flower, now a more convenient breed of cattle. We improve them gradually, because our ideals are vague and tentative, and our knowledge is very limited; because Nature, too, is shy and slow in our clumsy hands. Some day all this will be better organized, and still better. That is the drift of the current in spite of the eddies. The whole world will be intelligent, educated, and co-operating; th...

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Section: Developmental Encoding and Software Engineeringmentioning

confidence: 99%

The mismeasure of machine: Synthetic biology and the trouble with engineering metaphors

Boudry

Pigliucci

2013

Studies in History and Philosophy of Science Part C: Studies in

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“…According to Hartmann et al (2007), artificial development is increasingly being used to solve computational problems outside of biology by direct analogy with biological systems. The results indicate that replacing direct genetic encoding with indirect developmental encoding dramatically reduces the search space for evolutionary algorithms.…”

Section: Why Development? Gene Network Developmental Encoding and Tmentioning

confidence: 99%

Genotype–phenotype mapping and the end of the ‘genes as blueprint’ metaphor

Pigliucci

2010

Phil. Trans. R. Soc. B

226

150

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In a now classic paper published in 1991, Alberch introduced the concept of genotype -phenotype (G!P) mapping to provide a framework for a more sophisticated discussion of the integration between genetics and developmental biology that was then available. The advent of evo-devo first and of the genomic era later would seem to have superseded talk of transitions in phenotypic space and the like, central to Alberch's approach. On the contrary, this paper shows that recent empirical and theoretical advances have only sharpened the need for a different conceptual treatment of how phenotypes are produced. Old-fashioned metaphors like genetic blueprint and genetic programme are not only woefully inadequate but positively misleading about the nature of G!P, and are being replaced by an algorithmic approach emerging from the study of a variety of actual G!P maps. These include RNA folding, protein function and the study of evolvable software. Some generalities are emerging from these disparate fields of analysis, and I suggest that the concept of 'developmental encoding' (as opposed to the classical one of genetic encoding) provides a promising computational -theoretical underpinning to coherently integrate ideas on evolvability, modularity and robustness and foster a fruitful framing of the G!P mapping problem.

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“…Kshirsagar and Ratrikar [10] proposed a novel fault tolerant algorithm for tolerating stuckat faults in digital circuits. Hartmann, et al [11] use evolutionary method to automatically synthesize fault-tolerant and noise robust circuits. The genotypic complexity is measured in their work.…”

Section: Introductionmentioning

confidence: 99%

Negatively-correlated redundancy circuits evolution: A novel way of robust analog circuit synthesizing

Liu

Jiang

2010

Third International Workshop on Advanced Computational Intelligence

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Integrated circuit technology has been dramatically developed during the past decades. Reliability of analog circuits becomes more and more important for yield design. However, most state of the art researches are concentrated on analog circuit robust optimization and analog circuit fault detection. There are seldom any researches on analog circuit fault-tolerant design. This paper proposed an negativelycorrelated evolution strategy to design fault-tolerant analog circuits. In the proposed strategy, the fault-tolerant circuit can be considered as an ensemble system. The system contains several child circuits. negatively-correlated evolution strategy automatically synthesizes these child circuits, and make sure the errors of each child circuits are negatively correlated to each other. The experimental result shows that, in most circumstances, the circuit with negative correlation redundancy has the best fault tolerant performance compared with the circuit with randomly generated redundancy. The the circuit with duplicated redundancy didn't has much improvement compared with single circuit. So, the proposed strategy is a beneficial idea towards robust and fault tolerant analog circuit design.

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The genotypic complexity of evolved fault-tolerant and noise-robust circuits

Cited by 7 publications

References 8 publications

The mismeasure of machine: Synthetic biology and the trouble with engineering metaphors

The mismeasure of machine: Synthetic biology and the trouble with engineering metaphors

Genotype–phenotype mapping and the end of the ‘genes as blueprint’ metaphor

Negatively-correlated redundancy circuits evolution: A novel way of robust analog circuit synthesizing

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