computer science

HeuristicLab

HeuristicLab is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand HeuristicLab rather than just read about it. In short: HeuristicLab is a software environment for heuristic and evolutionary algorithms, developed by members of the Heuristic and Evolutionary Algorithm Laboratory (HEAL) at the University of Applied Sciences Upper Austria, in Hagenberg im Mühlkreis. HeuristicLab has a strong focus on providing a graphical user interface so that users are not required to have comprehensive programming skills to adjust and extend the algor…

Key takeaways

HeuristicLab belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect HeuristicLab to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of HeuristicLab from memory before moving on to harder problems.

Reference excerpt

HeuristicLab is a software environment for heuristic and evolutionary algorithms, developed by members of the Heuristic and Evolutionary Algorithm Laboratory (HEAL) at the University of Applied Sciences Upper Austria, in Hagenberg im Mühlkreis. HeuristicLab has a strong focus on providing a graphical user interface so that users are not required to have comprehensive programming skills to adjust and extend the algorithms for a particular problem. In HeuristicLab algorithms are represented as operator graphs and changing or rearranging operators can be done by drag-and-drop without actually writing code. The software thereby tries to shift algorithm development capability from the software engineer to the user and practitioner. Developers can still extend the functionality on code level and can use HeuristicLab's plug-in mechanism that allows them to integrate custom algorithms, solution representations or optimization problems.

History Development on HeuristicLab was started in 2002 by Stefan Wagner and Michael Affenzeller. The main motivation for the development of HeuristicLab was to build a paradigm-independent, flexible, extensible, and comfortable environment for heuristic optimization on top of a state-of-the-art programming environment and by using modern programming concepts. As the Microsoft .NET framework seemed to fulfill this requirements it was chosen as the development environment and C# as programming language. The first officially available version of HeuristicLab was 1.0 released in 2004 with an improved version 1.1 released in 2005. Development on the next version of HeuristicLab started in the same year. Among many things it was decided that HeuristicLab 2.0 should provide an entirely new user experience and lift the burden of programming off of the user. Therefore, HeuristicLab 2.0 was the first version featuring graphical tools for creating algorithms, however due to the complexity of the user interface HeuristicLab 2.0 was never released to the public. In the summer of 2007 it was decided that a new iteration of HeuristicLab was needed which should combine the usability of version 1.1 with the algorithm modeling concepts of version 2.0. HeuristicLab 3.0 was released internally in the beginning of 2008. In the next 2 years HeuristicLab was gradually improved which led to the release of version 3.3 in summer 2010 as open source software.

Features

Algorithm Designer One of the features that distinguishes HeuristicLab from many other metaheuristic software frameworks is the algorithm designer. HeuristicLab allows to model algorithms in a graphical way without having to write any source code. Algorithms in HeuristicLab are a composition of operators which are chained together. This sequence of operators is called the operator graph and can be viewed and edited for any algorithm in HeuristicLab. HeuristicLab also offers a so called Programmable Operator that can include source code which can be written from within HeuristicLab. Seeing how other algorithms work allows to try out new ideas by starting from an existing algorithm and incrementally modifying it. Of course new algorithms can also be created by writing code. Experiment Designer In HeuristicLab's Experiment Designer different algorithms with different parameter settings and problems can be composed, executed and analyzed. This is very useful for parameter tuning tasks where different parameterizations have to be executed and compared. HeuristicLab offerers a number of tools for graphically analyzing the results. Plugin Infrastructure Every functionality in HeuristicLab is available as a plugin. Developers can create and reuse plugins to integrate new features and extend the functionality of HeuristicLab. Some other features

Genetic programming models can be simplified. The genetic programming trees can be exported to MATLAB, LaTeX, Excel or other formats. Algorithms, problems, experiments, and results can be saved. Algorithms can be executed, pause, saved, restored, and continued. Algorithms and experiments can be executed in parallel on multi-core and distributed computing systems. Charts can be customized and exported to various image formats. Results and other data can be copied to and from Microsoft Excel or other applications. Write and solve MIP/LP models with integrated Google OR-Tools HeuristicLab can be coupled with external applications, such as simulation models, to optimize their parameters. Support for distributed computing (HeuristicLab Hive) based on a master-slave model similar to e.g. Boinc

Supported algorithms The following list gives an overview of the algorithms supported by HeuristicLab:

Genetic algorithm-related Genetic Algorithm Age-layered Population Structure (ALPS) Genetic Programming Evolution strategy CMA-ES Island Genetic Algorithm Island Offspring Selection Genetic Algorithm RAPGA SASEGASA Offspring Selection Evolution Strategy (OSES) Offspring Selection Genetic Algorithm Non-dominated Sorting Genetic Algorithm II Ensemble Modeling Gaussian Process Regression and Classification Gradient Boosted Trees Gradient Boosted Regression Local Search Particle Swarm Optimization Parameter-less population pyramid (P3) Robust Taboo Search Scatter Search Simulated Annealing Tabu Search Variable Neighborhood Search Performance Benchmarks Cross Validation k-Means Linear Discriminant Analysis Linear Regression Nonlinear Regression Multinomial Logit Classification Nearest Neighbor Regression and Classification Neighborhood Components Analysis Neural Network Regression and Classification Random Forest Regression and Classification Support Vector Regression and Classification Elastic-Net Kernel Ridge Regression Decision Tree Regression Barnes-Hut t-SNE User-Defined Algorithm: Allows to model algorithms within HeuristicLab's graphical modeling tools.

Supported problems The following list gives an overview of the problems supported by HeuristicLab:

… excerpt ends here. Continue reading the full article.

Illustrations

Worked examples

Example 1 — a first encounter with HeuristicLab

Start with the simplest possible case. Write down what HeuristicLab claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to HeuristicLab before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about HeuristicLab ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of HeuristicLab

In research: HeuristicLab appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses HeuristicLab in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: HeuristicLab is common in secondary-school and first-year university syllabi. It links to neighbouring topics Heuristic algorithms, so understanding it makes those chapters shorter.
In everyday life: Look for HeuristicLab outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study HeuristicLab in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what HeuristicLab means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain HeuristicLab out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is HeuristicLab in simple terms?

Why does HeuristicLab matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study HeuristicLab?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on HeuristicLab.