I have studied about Non dominating sorting algorithtm (nsga-II).
Algorithm is given on this link .
http://church.cs.virginia.edu/genprog/images/2/2f/Nsga_ii.pdf
I want to know it's real life application with examples.....I tried to search on the internet ,but no where found it.
If you have any ideas or relevent data/link ,please share with me.
You can find some real-life applications by just searching : "NSGA-II + applications" in Google Scholar : http://scholar.google.com/scholar?start=10&q=nsga-ii+application&hl=en&as_sdt=0,5
The ones who proposed NSGA-II are, indeed, Prof. Kalyanmoy Deb and his co-authors Samir Agrawal, Amrit Pratap and T. Meyarivan.
In my own research, I surveyed a number of NSGA-II based approaches for the portfolio optimization problem (a financial engineering problem), you can find a paper at the link : https://editorialexpress.com/cgi-bin/conference/download.cgi?db_name=CEF2012&paper_id=167
In my own, personal experience, I've used NSGA-II for two problems.
The Multi Objective Travelling salesman problem and
Community Detection in Networks.
These were mainly academic studies, so they can't be called real life applications.
For more concrete examples of NSGA-II in action, I know that, NSGA-II is used in optimization of chemical processes. Prof. S.K. Gupta, from whom I learnt about NSGA-II, did so himself and you can check out some of the practical applications in his list of papers
http://www.iitk.ac.in/che/Publ%20List%20SKG%20June%202012.pdf particularly paper #160, 163, 164, 177 and 187.
I'm not sure, but the inventor himself, Prof. Kalyanmoy Deb , also uses it in the field of Mechanical Engineering.
Basically, you can use it, in any Industrial Process, where optimization is required, be it a chemical process, or the design of car parts.
I used NSGA-II in a multi-objective evolutionary approach to optimize an artificial neural network that corresponds to a computational model of a part of the brain which is supposed to be a low-level system for action selection. If you are interested you may find more information on http://francky.me/publications.php#mRF2011
Note that any other Pareto-compliant ranking method would have probably worked.
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I need to develop a Course Timetabling software which can allot timeslots and rooms efficiently. This is a curriculum based routine, not post-enrollment based. And efficiently means classes are assigned timeslots according to staff time preferences and also need to minimize 1st year-2nd year class overlap so that 2nd year students can retake the courses they've failed to pass.(and also for 3rd-4th yr pair).
Now, at first i thought that would be an easy problem, but now it seems different. Most of the papers i've looked on uses Genetic Algorithm/PSO/Simulated Annealing or these type of algorithm. And i'm still unable to interpret the problem to a GA problem.
what i'm confused about is why almost none of them suggests DFS or Graph-coloring algorithm?
Can someone explain the scenario if DFS/graph-coloring is used? Or why they aren't suggested or tried.
My experience with solving this problem for a complex department, is that the hard constraints (like no overlapping of courses that are taken by the same population, and hard constraints of the teachers) are rather easily solvable by exact methods. I modeled the problem with 0-1 integer linear programming, and solved it with a SAT-based tool called minisat+. Competitive commercial tools like cplex can also solve it.
So with today's tools there is no need to approximate as suggested above, even when the input is rather large.
Now, optimizing the solution is a different story. There can be many (weighted) objectives, and finding the solution that brings the objective to minimum is indeed very hard computationally (no tool that I tried can solve it within 24 hours), but they reach near optimum in a few hours (I know it is near optimum because I can compute the theoretical bound on the solution).
This document describes applying a GA approach to university time-tabling, so it should be directly applicable to your requirement: Using a GA to solve university time-tabling
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I have been learning alot about using graphs for machine learning by watching Christopher Bishops videos( http://videolectures.net/mlss04_bishop_gmvm/ ). I find it very interesting and watched a few others in the same categories(machine learning/graph) but was wondering if anyone had any recommendations for ways of learning more?
My problem is, although the videos gave a great high level understanding, I don't have much practical skills in it yet. I've read Bishops book on machine learning/patterns as well as Norvig's AI book but both don't seem to touch upon specific using graphs much. With the emergence of search engines and social networking, I would think machine learning on graphs would be popular.
If possible, can anyone suggestion an a resource to learn from? (I'm new to this field and development is a hobby for me, so I'm sorry in advance if there's a super obvious resource to learn from..I tried google and university sites).
Thanks in advance!
First, i would strongly recommend the book Social Network Analysis for Startups by Maksim Tsvetovat and Alexander Kouznetsov. A book like this is a godsend for programmers who need to quickly acquire a basic fluency in a specific discipline (in this case, graph theory) so that they can begin writing code to solve problems in this domain. Both authors are academically trained graph theoreticians but the intended audience of their book is programmers. Nearly all of the numerous examples presented in the book are in python using the networkx library.
Second, for the projects you have in mind, two kinds of libraries are very helpful if not indispensible:
graph analysis: e.g., the excellent networkx (python), or igraph
(python, R, et. al.) are two that i can recommend highly; and
graph rendering: the excellent graphViz, which can be used
stand-alone from the command line but more likely you will want to
use it as a library; there are graphViz bindings in all major
languages (e.g., for python there are at least three i know of,
though pygraphviz is my preference; for R there is rgraphviz which is
part of the bioconductor package suite). Rgraphviz has excellent documentation (see in particular the Vignette included with the Package).
It is very easy to install and begin experimenting with these libraries and in particular using them
to learn the essential graph theoretic lexicon and units of analysis
(e.g., degree sequence distribution, nodes traversal, graph
operators);
to distinguish critical nodes in a graph (e.g., degree centrality,
eigenvector centrality, assortivity); and
to identify prototype graph substructures (e.g., bipartite structure,
triangles, cycles, cliques, clusters, communities, and cores).
The value of using a graph-analysis library to quickly understand these essential elements of graph theory is that for the most part there is a 1:1 mapping between the concepts i just mentioned and functions in the (networkx or igraph) library.
So e.g., you can quickly generate two random graphs of equal size (node number), render and then view them, then easily calculate for instance the average degree sequence or betweenness centrality for both and observer first-hand how changes in the value of those parameters affects the structure of a graph.
W/r/t the combination of ML and Graph Theoretic techniques, here's my limited personal experience. I use ML in my day-to-day work and graph theory less often, but rarely together. This is just an empirical observation limited to my personal experience, so the fact that i haven't found a problem in which it has seemed natural to combine techniques in these two domains. Most often graph theoretic analysis is useful in ML's blind spot, which is the availability of a substantial amount of labeled training data--supervised ML techniques depend heavily on this.
One class of problems to illustrate this point is online fraud detection/prediction. It's almost never possible to gather data (e.g., sets of online transactions attributed to a particular user) that you can with reasonable certainty separate and label as "fraudulent account." If they were particularly clever and effective then you will mislabel as "legitimate" and for those accounts for which fraud was suspected, quite often the first-level diagnostics (e.g., additional id verification or an increased waiting period to cash-out) are often enough to cause them to cease further activity (which would allow for a definite classification). Finally, even if you somehow manage to gather a reasonably noise-free data set for training your ML algorithm, it will certainly be seriously unbalanced (i.e., much more "legitimate" than "fraud" data points); this problem can be managed with statistics pre-processing (resampling) and by algorithm tuning (weighting) but it's still a problem that will likely degrade the quality of your results.
So while i have never been able to successfully use ML techniques for these types of problems, in at least two instances, i have used graph theory with some success--in the most recent instance, by applying a model adapted from the project by a group at Carnegie Mellon initially directed to detection of online auction fraud on ebay.
MacArthur Genius Grant recipient and Stanford Professor Daphne Koller co-authored a definitive textbook on Bayesian networks entitled Probabalistic Graphical Models, which contains a rigorous introduction to graph theory as applied to AI. It may not exactly match what you're looking for, but in its field it is very highly regarded.
You can attend free online classes at Stanford for machine learning and artificial intelligence:
https://www.ai-class.com/
http://www.ml-class.org/
The classes are not simply focused on graph theory, but include a broader introduction in the field and they will give you a good idea of how and when you should apply which algorithm. I understand that you've read the introductory books on AI and ML, but I think that the online classes will provide you with a lot of exercises that you can try.
Although this is not an exact match to what you are looking for, textgraphs is a workshop that focuses on the link between graph theory and natural language processing. Here is a link. I believe the workshop also generated this book.
I'm searching for relative (after 2000) new papers about TSP.
All the papers I have found were quite hard and needed high level
mathematical skills. I'm looking for papers that are simple to read
for someone who has simple college mathematic knowledge and good
programming knowledge in Java and C (I didn't find any current paper
implementing TSP with these languages).
Any hints will be highly appreciated.
(edit)
What Im trying to say is that im searching for papers that dont need to understand
difficult formulas. For instance some papers describe algorithms, or the philosophy
of solution. It isnt necessary to implement that algorithm, just describe the techniques.
Maybe using some simple geometry ...
I found some papers based on Lin-Kernighan methods, which seemed ok ...
I'm somewhat familiar with the TSP literature, and I doubt there's anything matching your criteria; the simpler, less mathematical algorithms were pioneered long before your cutoff.
David S. Johnson and coauthors have some articles that I like: http://www2.research.att.com/~dsj/papers.html , in particular #1 and #3 under Traveling Salesman Problem.
If you want simple, here is one page that describes using space filling curves to find a good solution (not optimal obviously). Of course, a webpage is not a paper. Nor do I know if the ideas presented there were created after 2000. If this is more of what you are looking for, perhaps you should edit your question, or provide an example.
http://www2.isye.gatech.edu/~jjb/mow/mow.html
Here's what you can do:
1) Study chapter 11-Guided Local Search and Chapter 12- Iterated Local Search from Handbook of Metaheuristics(2010), each of these has a section describing how GLS and ILS are designed for TSP. Both ILS and GLS are interesting and quite easy to implement.
2) Check this paper: "Guided local search and its application to the traveling salesman problem"
3) Find the Ruby code for these algorithms here , and rewrite it in Java
In industry, there is often a problem where you need to calculate the most efficient use of material, be it fabric, wood, metal etc. So the starting point is X amount of shapes of given dimensions, made out of polygons and/or curved lines, and target is another polygon of given dimensions.
I assume many of the current CAM suites implement this, but having no experience using them or of their internals, what kind of computational algorithm is used to find the most efficient use of space? Can someone point me to a book or other reference that discusses this topic?
After Andrew in his answer pointed me to the right direction and named the problem for me, I decided to dump my research results here in a separate answer.
This is indeed a packing problem, and to be more precise, it is a nesting problem. The problem is mathematically NP-hard, and thus the algorithms currently in use are heuristic approaches. There does not seem to be any solutions that would solve the problem in linear time, except for trivial problem sets. Solving complex problems takes from minutes to hours with current hardware, if you want to achieve a solution with good material utilization. There are tens of commercial software solutions that offer nesting of shapes, but I was not able to locate any open source solutions, so there are no real examples where one could see the algorithms actually implemented.
Excellent description of the nesting and strip nesting problem with historical solutions can be found in a paper written by Benny Kjær Nielsen of University of Copenhagen (Nielsen).
General approach seems to be to mix and use multiple known algorithms in order to find the best nesting solution. These algorithms include (Guided / Iterated) Local Search, Fast Neighborhood Search that is based on No-Fit Polygon, and Jostling Heuristics. I found a great paper on this subject with pictures of how the algorithms work. It also had benchmarks of the different software implementations so far. This paper was presented at the International Symposium on Scheduling 2006 by S. Umetani et al (Umetani).
A relatively new and possibly the best approach to date is based on Hybrid Genetic Algorithm (HGA), a hybrid consisting of simulated annealing and genetic algorithm that has been described by Wu Qingming et al of Wuhan University (Quanming). They have implemented this by using Visual Studio, SQL database and genetic algorithm optimization toolbox (GAOT) in MatLab.
You are referring to a well known computer science domain of packing, for which there are a variety of problems defined and research done, for both 2-dimnensional space as well as 3-dimensional space.
There is considerable material on the net available for the defined problems, but to find it you knid of have to know the name of the problem to search for.
Some packages might well adopt a heuristic appraoch (which I suspect they will) and some might go to the lengths of calculating all the possibilities to get the absolute right answer.
http://en.wikipedia.org/wiki/Packing_problem
Are there any "special" image compression algorithms for face cases? So i'm creating a conference programm I want to transfer images (or videos) of talking heads through the internet. Are there any special algorithms to compress images/videos of talking heads so to make them smaller (like special voice compression algorithms)?
Really not my field, but I know that a couple of researchers from my university work on a compression algorithm that shows impressive results on limited domains (such as faces). They have published a few articles explaining the algorithms, as well as released a Matlab plugin for it.
The algorithm is called K-SVD, and you can read more about it in the basic article. There are many follow-up articles published as well.
For the implementation, check out:
Prof. Elad's Software page
Prof. Rubinstein Software Page
Jörgen Ahlberg at Linköping University, Sweden, wrote a PhD thesis on the subject in 2002:
J. Ahlberg, Model-based Coding - Extraction, Coding, and Evaluation of Face Model Parameters , PhD Thesis No. 761, Dept. of Electrical Engineering, Linköping University, Sweden, 2002.
It appears the technology has been moved to http://www.visagetechnologies.com/
Edit
The algorithm Jören developed is called Candide-3, more info on the algorithm is available at http://www.icg.isy.liu.se/candide/