Do you have recommendations for a book that presents the different algorithm use

Question

Do you have recommendations for a book that presents the different algorithm used in theoretical evolutionary biology?

I don't mean evolutionary or genetic algorithms (otherwise this question would not be a good fit for Biology.SE) but algorithms applied to evolutionary biology. I am not interested in statistical procedures and algorithms to reconstruct phylogenetic trees, to annotate DNA sequences or to find out synonymous changes by comparing sequences of closely related species. I am not interested in introductory book on programming.

I am interested to computational modeling applied population genetics, kin selection, game theory, population range expansion, simulating sexual reproduction, selection for different sex determination system, evolution for robustness/evolvability, evolution of codon usage, evolution of genetic code, evolution of cognition, evolution of multicellularity,

Explanation / Answer

What you are describing usually falls under the category of computational biology or just mathematical biology. Unfortunately, the biggest part of this field is bioinformatics, or the application of statistical and/or dynamical programming techniques to sequence data. You exclude this in your question, and I would agree with you that it is a "boring" topic from a theoretical biologist's perspective, because it mostly uses computer science in a very standard way as a tool for experimental biologists.

As you noticed, it is also standard to use computational models in theory papers as a way of simulating things. I think this is the sort of resources you are asking for. Unfortunately, specific algorithms are seldom standardized or reused in this area. Typically, each paper (or sequence of related papers) uses their own models.[1] As you read a lot of papers, you will find some common themes, but these are just standard ideas of theoretical biology expressed as simulation. I doubt that comprehensive books exist, and even if some do their utility is not clear to me due to a lack of specific algorithm (re)use.[2] However, the most commonly used techniques are to either to solve differential equations numerically (which most would classify as mathematical modeling) or run agent-based or population-based models. General resources on these exist, and here are some discussions for the latter other SEs:

Simulation modeling of diseases.
Sources for Algorithmic Evolutionary Game Theory.
Canonical reference on agent-based computing.

Finally, do not confuse 'computational modeling in theoretical biology' with 'algorithmic biology'. Algorithmic biology is a new field that views ecological and evolutionary dynamics as computatioanl proccesses. Instead of using mathematical tools borrowed from physics (as is standard with the dynamic systems approaches to mathematical biology), it uses the tool of theoretical computer science (note that this is a type of math that has very little to do with programming the laptop infront of you). I know of only two books in this field:

Gregory Chaitin "Proving Darwin: Making biology mathematical" - Although Chaitin had contributed significant and original thoughts to computer science in the past, I would strongly advice against this book because it misses the point from both the biological and computer science point of view.

Leslie Valiant "Probably Approximately Correct: Nature's algorithms for learning and prospering in a complex World" - in this book (based on earlier papers), Valiant recasts evolution as a type of machine learning. This is very interesting from a mathematical point of view, although it might not connect directly enough to biologically relevant questions (see also some of the discussion in Kaznatcheev (2013)). I would recommend reading this book.

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