Emergence of algorithmic language in genetic systems

Palacios, Otoniel; Stephens, Christopher R.; Waelbroeck, Henri

doi:10.1016/s0303-2647(98)00007-0

Cited by 9 publications

(2 citation statements)

References 20 publications

(14 reference statements)

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“…By use of a system that encodes the phenotype with a grammar-like genotype, a large variability of exploration strategies for a fixed phenotype and a self-adaptive drift towards short representations were observed. Palacios et al [39] considered a neurogenetic model with a genotype-phenotype map of a highly degenerate nature. Despite the nature of the mapping, certain genotypes were consistently preferred, thus indicating that the genotype-phenotype symmetry was broken.…”

Section: Genotype-phenotype Mappingmentioning

confidence: 99%

Specializing Context-Free Grammars With a (1 + 1)-EA

Manzoni

Bartoli

Castelli

et al. 2020

IEEE Trans. Evol. Computat.

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Section: Genotype-phenotype Mappingmentioning

confidence: 99%

Specializing Context-Free Grammars With a (1 + 1)-EA

Manzoni

Bartoli

Castelli

et al. 2020

IEEE Trans. Evol. Computat.

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“…Today the chemistry of gene switching and its developmental outcomes can be studied empirically and then simulated in detail on powerful computers (e.g. McAdams and Arkin, 1997;Palacios et al, 1998;Gardner et al, 2000;Davidson et al, 2002;Rao et al, 2004). The learning rules of the brain are now treated by an entire mathematical science called neural network engineerng (e.g.…”

Section: Presentmentioning

confidence: 99%

Genes, Mind, and Culture - The Coevolutionary Process: 25th Anniversary Edition

Lumsden¹,

Wilson²

2005

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The Evolutionary Language Game

Nowak

Plotkin

Krakauer

1999

Journal of Theoretical Biology

165

133

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We explore how evolutionary game dynamics have to be modified to accomodate a mathematical framework for the evolution of language. In particular, we are interested in the evolution of vocabulary, that is associations between signals and objects. We assume that successful communication contributes to biological fitness: individuals who communicate well leave more offspring. Children inherit from their parents a strategy for language learning (a language acquisition device). We consider three mechanisms whereby language is passed from one generation to the next: (i) parental learning: children learn the language of their parents; (ii) role model learning: children learn the language of individuals with a high payoff; and (iii) random learning: children learn the language of randomly chosen individuals. We show that parental and role model learning outperform random learning. Then we introduce mistakes in language learning and study how this process changes language over time. Mistakes increase the overall efficacy of parental and role model learning: in a world with errors evolutionary adaptation is more efficient. Our model also provides a simple explanation why homonomy is common while synonymy is rare.

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Emergence of algorithmic language in genetic systems

Cited by 9 publications

References 20 publications

Specializing Context-Free Grammars With a (1 + 1)-EA

Specializing Context-Free Grammars With a (1 + 1)-EA

Genes, Mind, and Culture - The Coevolutionary Process: 25th Anniversary Edition

The Evolutionary Language Game

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