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I am happy to announce that our group has moved into our new offices in the new building 52 with the nice name [合肥学院综合实验楼]. While the new offices are still temporary, they are very nice and modern. We not just have much more space than before, all the members of our young group can now also finally be co-located, which will make collaboration much easier. We are very thankful for the support of our university and faculty. The new building is quite beautiful, modern, and even equipped with solar panels on the facade! Our campus is now growing quickly and, with the generous support of the city and the government, our university adds more and more facilities and buildings.

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1. Introduction

A long time ago, when I was a PhD student, I wrote the book Global Optimization Algorithms – Theory and Application, which I published on my personal website as pdf. Since I recently developed the short course Metaheuristics for Smart Manufacturing and had a very nice experience teaching it, I have decided to begin to write a new book about optimization, "An Introduction to Optimization Algorithms," to incorporate my experience during the past ten years working in the field. When writing such a book, there are a couple of desirable features to improve the workflow and results, such as:

  • using a version control and distributed authoring system, which allows me to easily work on and extend the book as well as to make changes to the book and commit them wherever I am,
  • automated conversion of the book's sources to PDF, ideally whenever I commit a change,
  • automated provision of the PDF version of the book at an online location,
  • the generation of an electronic version of the book more suitable for handheld devises like mobile phones, i.e., an EPUB version, which should be automatically be built and provisioned like the PDF version,
  • maybe even the possibility that readers can file change requests, ask questions, or propose content to add in a structured way.

In this post, I want to discuss a process, workflow, and tool support I came up with to achieve all of these features. This workflow has been designed in a way so that you can use it too, easily, for your own open books. Here we first describe the setup, which is stuff you have to do once and only once. After that, we discuss the features and commands that you have available inside your book.

2. The Setup

First, both for writing and hosting the book, we use a GitHub repository, very much like the one for the book I just began working on here. The book should be written in Pandoc's markdown syntax, which allows us to include most of the stuff we need, such as equations and citation references. For building the book, we will use Travis CI, which offers a free integration with GitHub to build open source software – triggered by repository commits. So every time you push a commit to your book repository, Travis CI will be notified, and check out the repository. But we have to tell Travis CI what to do with out book's sources, namely to compile them with Pandoc, which I have packaged with all necessary tools and filters into a docker container. Once Travis has finished downloading the container and building the book with it, it can "deploy" the produced files. For this purpose, we make use of GitHub Pages, a feature of GitHub which allows you to have a website for a repository. So we simply let Travis deploy the compiled book, in PDF and EPUB, to the book's repository website. Once the repository, website, and Travis build procedure are all set up, we can concentrate on working on our book and whenever some section or text is finished, commit, and enjoy the automatically new versions. Since the book's sources are available as GitHub repository, our readers can file issues to the repository, with change suggestions, discovered typos, or with questions to add clarification. They may even file pull requests with content to include.

2.1. The Repository

In order to use my workflow, you need to first have an account at GitHub and then create an open repository for your book. GitHub is built around the distributed version control system git, for which a variety of graphical user interfaces exist – see, e.g., of here. If you have a Debian-based Linux system, you can install the basic git client with command line interface as follows: sudo apt-get install git. You can use either this client or such a GUI to work with your repository. I suggest that in your main branch of the repository, you put a folder book where all the raw sources and graphics for your book go. In the repository root folder, you can then leave the non-book-related things, like, .travis.yml, and At this step, you should choose a license for your project, maybe a creative commons one, if you want.

You should now put a file named "" into the "book" folder of your repository, it could just contain some random text for now, the real book comes later.

2.2. The gh-pages Branch

Since we want the book to be automatically be built and published to the internet, we should have a gh-pages branch in our repository as well. I assume that you have a Unix/Linux system with git installed. In that case, you can do this as follows (based on here and here), by replacing YOUR_USER_NAME with your user name and YOUR_REPOSITORY with your repository name:

git clone --depth=50 --branch=master YOUR_USER_NAME/YOUR_REPOSITORY
git checkout --orphan gh-pages
ls -la |awk '{print $9}' |grep -v git |xargs -I _ rm -rf ./_ 
git rm -rf .
git commit --allow-empty -m "root commit"
git push origin gh-pages

You can now safely delete the folder YOUR_USER_NAME/YOUR_REPOSITORY that was created during this procedure. If you go to the settings page of your repository, it should now display something like " Your site is published at" under point "GitHub Pages". This is where your book will later go.

2.3. Personal Access Token

Later, we will use Travis CI to automatically build your book and to automatically deploy it the GitHub pages branch of your repository. For the latter, Travis will need a so-called personal access token, as described here. You need to create such a token following the steps detailed here. Basically, you go to your personal settings page for your GitHub account, click "Developer Settings" and then "Personal Access Tokens". You click "Generate new token" and confirm your password. Then you need to choose public_repo and click "Generate token". Make sure you store the token as text somewhere safe, we need this token text later on.

2.4. Travis CI: Building and Deployment

With that in place, we can now setup Travis CI for automated building and deployment. You can get a Travis account easily and even sign in with GitHub. When you sign into Travis, it should show you a list with your public GitHub repositories. You need to should enable your book repository for automated build.

Click on the now-activated repository in Travis and click "More Settings". Scroll down to "Environment Variables" and then add a variable named "GITHUB_TOKEN". As value, copy the text of the personal access token that we have created in the previous step.

2.5. .travis.yml

Now we need to finally tell Travis how to build our book, and this can be done by placing a file called .travis.yml into the root folder of your GitHub repository. This file should have the following contents, where YOUR_BOOK_OUTPUT_BASENAME should be replaced with the base name of the files to be generated (e.g., "myBook" will result in "myBook.pdf" and "myBook.epub" later):

sudo: required

language: generic

  - docker

- |
  baseDir="$(pwd)" &&\
  inputDir="${baseDir}/book" &&\
  outputDir="${baseDir}/output" &&\
  mkdir -p "${outputDir}" &&\
  docker run -v "${inputDir}/":/input/ \
             -v "${outputDir}/":/output/ \
             -t -i thomasweise/docker-bookbuilder YOUR_BOOK_OUTPUT_BASENAME &&\
  cd "${outputDir}"

  provider: pages
  skip-cleanup: true
  github-token: $GITHUB_TOKEN
  keep-history: false
    branch: master
  target-branch: gh-pages
  local-dir: output

After adding this file, commit the changes and push the commit to the repository. Shortly thereafter, a new Travis build should start. If it goes well, it will produce two files, namely "" and "" (where YOUR_USER_NAME will be the lower-case version of your user name). You can link them from the file that you probably have in your project's root folder.

3. The Workflow

In your book, you can use all the features and syntax of Pandoc's markdown. My system will also automatically load and apply the pandoc-citeproc filter, which allows you to use citations to a bibliography and the pandoc-crossref for numbering and referencing figures and tables.

The workhorse is an R package named bookbuildeR. This package provides a set of additional commands and pre-processing capabilities, which are detailed here. Most importantly, it allows you to specify one main markdown file and recursively include metadata and other markdown files using the command \relative.input{RELATIVE_PATH_TO_FILE}. It can also resolve relative paths towards the root document's location via \relative.path{RELATIVE_PATH_TO_FILE}, which is necessary to include figures located in a sub-folder of the current folder without specifying the complete path.

With these facilities, you should be able to build electronic books automatically in a nice way.

4. Infrastructure

While we have already discussed the interplay of GitHub and Travis CI to get your book compiled, we have omitted one more element of our infrastructure: Docker. Docker allows us to build something like very light-weight virtual machines (I know, they are not strictly virtual machines). For this purpose, we can build images, which are basically states of a file system. Our Travis builds load such an image, namely thomasweise/docker-bookbuilder, which provides my bookbuildeR R package on top of an R installation (thomasweise/docker-pandoc-r) on top of a Pandoc installation (thomasweise/docker-pandoc) on top of a TeX Live installation (thomasweise/docker-texlive-full). Of course, you could also use any of these containers locally or extend them in any way you want, in order to use different tools or book building procedures.

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Today, I had the great pleasure of teaching my short course Metaheuristics for Smart Manufacturing at the Quanzhou University of Information Engineering [泉州信息工程学院] in Quanzhou city [泉州市] in the Fujian province [福建省] as part of the 2018 Teacher Training Program of the Sino-German Center for Education Cooperation and Development of the Fujian Province [中德(福建)教育合作与发展师资培训项目] with the topic "Intelligent Manufacturing". The course was given as two 2-hour lectures to an audience of about 30 key teachers from more than 20 higher vocational colleges in the province who mainly specialized in the field of mechanical engineering.

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Today, on Thursday, July 26, 2018, our first Institute Workshop on Applied Optimization took place. Three talks were contributed by our researchers as well as our international guest researcher Dr. Mohammad Ali Raayatpanah [محمدعلی رعایت پناه] from the Kharazmi University [دانشگاه خوارزمی] in Iran [جمهوری اسلامی ایران]. We believe that a good way to good research is cooperation and the exchange of thoughts. It is especially good to discuss about research from different, but related fields. Each talk led to interesting conversations and hopefully has opened up opportunities for even more inspiring discussions and joint research.

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Today, our application for a Special Issue on Benchmarking of Computational Intelligence Algorithms was accepted by the Applied Soft Computing journal published by Elsevier B.V. and indexed by SCIE and EI. Here you can download the Call for Papers (CfP) of the Special Issue in PDF format and here as plain text file.

Computational Intelligence (CI) is a huge and expanding field which is rapidly gaining importance, attracting more and more interests from both academia and industry. It includes a wide and ever-growing variety of optimization and machine learning algorithms, which, in turn, are applied to an even wider and faster growing range of different problem domains. For all of these domains and application scenarios, we want to pick the best algorithms. Actually, we want to do more, we want to improve upon the best algorithm. This requires a deep understanding of the problem at hand, the performance of the algorithms we have for that problem, the features that make instances of the problem hard for these algorithms, and the parameter settings for which the algorithms perform the best. Such knowledge can only be obtained empirically, by collecting data from experiments, by analyzing this data statistically, and by mining new information from it. Benchmarking is the engine driving research in the fields of optimization and machine learning for decades, while its potential has not been fully explored. Benchmarking the algorithms of Computational Intelligence is an application of Computational Intelligence itself! This special issue of the EI/SCIE-indexed Applied Soft Computing journal published by Elsevier B.V. solicits novel contributions from this domain.

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