PonyGE2: Grammatical Evolution in Python

Grammatical Evolution (GE) is a population-based evolutionary algorithm, where a formal grammar is used in the genotype to phenotype mapping process. PonyGE2 is an open source implementation of GE in Python, developed at UCD's Natural Computing Research and Applications group. It is intended as an advertisement and a starting-point for those new to GE, a reference for students and researchers, a rapid-prototyping medium for our own experiments, and a Python workout. As well as providing the characteristic genotype to phenotype mapping of GE, a search algorithm engine is also provided. A number of sample problems and tutorials on how to use and adapt PonyGE2 have been developed.Comment: 8 pages, 4 figures, submitted to the 2017 GECCO Workshop on Evolutionary Computation Software Systems (EvoSoft

Iterated learning and grounding: from holistic to compositional languages

This paper presents a new computational model for studying the origins and evolution of compositional languages grounded through the interaction between agents and their environment. The model is based on previous work on adaptive grounding of lexicons and the iterated learning model. Although the model is still in a developmental phase, the first results show that a compositional language can emerge in which the structure reflects regularities present in the population's environment

Universal Grammar: Wittgenstein versus Chomsky

Daniele Moyal-Sharrock, ‘Universal Grammar: Wittgenstein versus Chomsky’ in M. A. Peters and J. Stickney, eds., A Companion to Wittgenstein on Education: Pedagogical Investigations (Singapore: Springer Verlag, 2017), ISBN: 9789811031342The motivations for the claim that language is innate are, for many, quite straightforward. The innateness of language is seen as the only way to solve the so-called 'logical problem of language acquisition': the mismatch between linguistic input and linguistic output. In this paper, I begin by unravelling several strands of the nativist argument, offering replies as I go along. I then give an outline of Wittgenstein's view of language acquisition, showing how it renders otiose problems posed by nativists like Chomsky – not least by means of Wittgenstein's own brand of grammar which, unlike Chomsky's, does not reside in the brain, but in our practices.Peer reviewe

Attention and empirical studies of grammar

How is the generation of a grammatical sentence implemented by the human brain? A starting place for such an inquiry lies in linguistic theory. Unfortunately, linguistic theories illuminate only abstract knowledge representations and do not indicate how these representations interact with cognitive architecture to produce discourse. We examine tightly constrained empirical methods to study how grammar interacts with one part of the cognitive architecture, namely attention. Finally, we show that understanding attention as a neural network can link grammatical choice to underlying brain systems. Overall, our commentary supports a multilevel empirical approach that clarifies and expands the connections between cognitive science and linguistics thus advancing the interdisciplinary agenda outlined by Jackendoff

Automated DNA Motif Discovery

Ensembl's human non-coding and protein coding genes are used to automatically find DNA pattern motifs. The Backus-Naur form (BNF) grammar for regular expressions (RE) is used by genetic programming to ensure the generated strings are legal. The evolved motif suggests the presence of Thymine followed by one or more Adenines etc. early in transcripts indicate a non-protein coding gene. Keywords: pseudogene, short and microRNAs, non-coding transcripts, systems biology, machine learning, Bioinformatics, motif, regular expression, strongly typed genetic programming, context-free grammar.Comment: 12 pages, 2 figure

An automatic generation of textual pattern rules for digital content filters proposal, using grammatical evolution genetic programming

AbstractThis work presents a conceptual proposal to address the problem of intensive human specialized resources that are nowadays required for the maintenance and optimized operation of digital contents filtering in general and anti-spam filtering in particular. The huge amount of spam, malware, virus, and other illegitimate digital contents distributed through network services, represents a considerable waste of physical and technical resources, experts and end users time, in continuous maintenance of anti-spam filters and deletion of spam messages, respectively. The problem of cumbersome and continuous maintenance required to keep anti-spam filtering systems updated and running in an efficient way, is addressed in this work by the means of genetic programming grammatical evolution techniques, for automatic rules generation, having SpamAssassin anti-spam system and SpamAssassin public corpus as the references for the automatic filtering customization

Phrase structure grammars as indicative of uniquely human thoughts

I argue that the ability to compute phrase structure grammars is indicative of a particular kind of thought. This type of thought that is only available to cognitive systems that have access to the computations that allow the generation and interpretation of the structural descriptions of phrase structure grammars. The study of phrase structure grammars, and formal language theory in general, is thus indispensable to studies of human cognition, for it makes explicit both the unique type of human thought and the underlying mechanisms in virtue of which this thought is made possible