Week 5 – Interdisciplinary Applications of Evolutionary Theory

Linguistics Overview

Okay, so I was thinking– we really did gloss over just about everything when we ran through linguistics this week. So here’s a brief glossary of particularly important terms:

Allomorphs: Two phonetically similar and semantically identical morphemes that never appear in the same context.
Allophones: Two sounds that are phonetically similar and never appear in the same context.
Cognate: Two words in related languages that are recognizably similar in both sound and in meaning.
Content words: Words that have specific, definable meanings; basically any word in a sentence that’s not a function word is a content word.
Function words: Words that only exist to serve the grammar of the language; they are often difficult to specifically define and short in length. Examples in English include “and”, “but”, “the”, etc.
Morpheme: The smallest linguistic unit that still has meaning.
Phoneme: A sound that is used by a language to distinguish between words.

That’s everything I can explain for now. Comment if you want more info, want additional terms defined/explained, whatever.

Crossover in biology and computer science

Last week, Rosie was talking about how biologists are uncertain about the benefits provided by sexual recombination. Particularly, she said something about how ‘the numbers’ don’t show any benefit to the organism for sexual recombination. If I recall correctly, sexual recombination is crossover (that’s how I’m going to treat it for this post and someone needs to correct me if I’m wrong).

Then, in my computer science class today, we were talking about Stochastic Local Search algorithms, of which Genetic Algorithms are a subset, and the lecture presented the introduction of crossover as the defining factor of GAs. The impression that I got was that, in computer science, crossover was assumed to be beneficial. And since computer scientists are generally good at calculating the generalized efficiency of algorithms, I assumed that there must be some fairly definite benefit provided by crossover, which contradicts what Rosie said about the mathematic benefits of biological crossover not adding up.

If GAs in computer science benefit from crossover and biological evolution doesn’t, then I see three possible outcomes. 1) The analogy between GAs and biology break down in this case – which is the default assumption. 2) The computer scientists haven’t done strict tests of crossover, and it will turn out to be unhelpful, as in biology. Or 3) GAs and biology both benefit from crossover, meaning that there will be mathematical models of the benefit of crossover from computer science which might be able to inform biology.

I have some discussion on these three possibilities. But I’m going to have to hold off for now.