Using R? to model a metaheuristic path choice in graph navigation
Ok so I am a complete newbie at most things programming. That being said I have been busting my head against several workflows to solve a problem I have. Where I am not asking anyone to do my work - suggestions to packages workflows etc, links to resources, and example code is extraordinarily helpful.

The problem is essentially "find an exhaustive and hierarchical(or ranked) set of pathways (or routes) that follow from most probable to least probable based off of a set of variables. So there will essentially be a starting node in this graph or a point of origin from which I would like to calculate theses routes from a set of weighted metrics that terminate at points where they path will hit "dead-ends" or boundarys forcing it to turn around- these variables are attached to the nodes as values in fields that I have already calculated using network analysis. The kicker is that I am having issues (albeit due to being such a novice in this domain) on several fronts. 

Before I get into the specific fundamental issues my current efforts has been to adapting a Ant Colony Optimization model that will accomplish this task but am obviously hitting walls in trying to implement this effectively because of the issues below. I have considered A*search and other pathfinding algorithms but they lack the capabilities I am looking for in discovering "most-likely to least-likely" off of more than just shortest distance. 

First - What should I use to accomplish this task in terms of programming language and platform? Obviously this requires me to state that I have access to MatLab, R, Mathmatica, and Python most readily and have been quasi introduced to R and Python on more intimate levels.
Right now I am currently focusing on R - but I should admit that the end goal is to have these routes in a final vector file format accessible in ArcGIS which would suggest Python being more applicable or both - I am not sure.

Second- What is the best data type or workflow to use for finding these routes? The data table I have used to calculate these variables is in a .csv table - with x,y coordinates which are georeferenced and associated with various fields (in columns) that are the values of the variables. There is no issue for me to convert these coordinates into point features in a shapefile or convert them into a raster image for each variable and overlay these images etc. But my primary inclination is to create a undirected graph with topological relationships from which I can implement something like Ant Colony Optimization to find these routes.

Some more issues I am having are that in just one .csv file I have apprx. 200,000 (x,y) vertices that do not have explicit edges denoted which connect the nodes - so building a graph manually is pretty much out of the question. I have somewhat accomplished creating these edges in ArcGIS but creating a lines from each node to its neighbors off of a distance and bearing radius. Which for these purposes are .4224m in distance and 360* in search threshold. To circumvent this taxing task via R in some manner is also preferable.

Also this task will have to be repeated - hundreds of times to various other dataset's. 

Thanks for your responses before hand and since I am on this task alone I appreciate any help I can get - this is a great site.
Using R? to model a metaheuristic path choice in graph navigation
===

Ok so I am a complete newbie at most things programming. That being said I have been busting my head against several workflows to solve a problem I have. Where I am not asking anyone to do my work - suggestions to packages workflows etc, links to resources, and example code is extraordinarily helpful.

The problem is essentially "find an exhaustive and hierarchical(or ranked) set of pathways (or routes) that follow from most probable to least probable based off of a set of variables. So there will essentially be a starting node in this graph or a point of origin from which I would like to calculate theses routes from a set of weighted metrics that terminate at points where they path will hit "dead-ends" or boundarys forcing it to turn around- these variables are attached to the nodes as values in fields that I have already calculated using network analysis. The kicker is that I am having issues (albeit due to being such a novice in this domain) on several fronts. 

Before I get into the specific fundamental issues my current efforts has been to adapting a Ant Colony Optimization model that will accomplish this task but am obviously hitting walls in trying to implement this effectively because of the issues below. I have considered A*search and other pathfinding algorithms but they lack the capabilities I am looking for in discovering "most-likely to least-likely" off of more than just shortest distance. 

First - What should I use to accomplish this task in terms of programming language and platform? Obviously this requires me to state that I have access to MatLab, R, Mathmatica, and Python most readily and have been quasi introduced to R and Python on more intimate levels.
Right now I am currently focusing on R - but I should admit that the end goal is to have these routes in a final vector file format accessible in ArcGIS which would suggest Python being more applicable or both - I am not sure.

Second- What is the best data type or workflow to use for finding these routes? The data table I have used to calculate these variables is in a .csv table - with x,y coordinates which are georeferenced and associated with various fields (in columns) that are the values of the variables. There is no issue for me to convert these coordinates into point features in a shapefile or convert them into a raster image for each variable and overlay these images etc. But my primary inclination is to create a undirected graph with topological relationships from which I can implement something like Ant Colony Optimization to find these routes.

Some more issues I am having are that in just one .csv file I have apprx. 200,000 (x,y) vertices that do not have explicit edges denoted which connect the nodes - so building a graph manually is pretty much out of the question. I have somewhat accomplished creating these edges in ArcGIS but creating a lines from each node to its neighbors off of a distance and bearing radius. Which for these purposes are .4224m in distance and 360* in search threshold. To circumvent this taxing task via R in some manner is also preferable.

Also this task will have to be repeated - hundreds of times to various other dataset's. 

Thanks for your responses before hand and since I am on this task alone I appreciate any help I can get - this is a great site.