computer science

Genetic improvement (computer science)

Genetic improvement (computer science) 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 Genetic improvement (computer science) rather than just read about it. In short: In computer software development, genetic improvement is the use of optimisation and machine learning techniques, particularly search-based software engineering techniques such as genetic programming to improve existing software. The improved program need not behave identically to the original.

Key takeaways

Genetic improvement (computer science) 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 Genetic improvement (computer science) to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Genetic improvement (computer science) from memory before moving on to harder problems.

Reference excerpt

In computer software development, genetic improvement is the use of optimisation and machine learning techniques, particularly search-based software engineering techniques such as genetic programming to improve existing software.

The improved program need not behave identically to the original. For example, automatic bug fixing improves executable code by reducing or eliminating buggy behaviour. In other cases the improved software should behave identically to the old version but is better because, for example: it runs faster, it uses less memory, it uses less energy or it runs on a different type of computer. GI differs from, for example, formal program translation, in that it primarily verifies the behaviour of the new mutant version by running both the new and the old software on test inputs and comparing their output and performance in order to see if the new software can still do what is wanted of the original program and is now better. Genetic improvement can be used to create multiple versions of programs, each tailored to be better for a particular use or for a particular computer. Genetic improvement can be used with multi-objective optimization to consider improving software along multiple dimensions or to consider trade-offs between several objectives, such as asking GI to evolve programs which trade speed against the quality of answers they give. Of course it may be possible to find programs which are both faster and give better answers. Mostly, genetic improvement makes typically small changes or edits (also known as mutations) to the program's source code but sometimes the mutations are made to assembly code, byte code or binary machine code.

References

External links Open PhD tutorial http://phdopen.mimuw.edu.pl/index.php?page=z15w1 (also covers SBSE and CIT but last of three topics is Genetic Improvement of software). International Workshops on Genetic Improvement: http://www.geneticimprovementofsoftware.com web pages include GI community pages http://geneticimprovementofsoftware.com/learn/about

Tools GIN https://github.com/gintool/gin Magpie https://github.com/bloa/magpie PyGGI https://github.com/coinse/pyggi

Worked examples

Example 1 — a first encounter with Genetic improvement (computer science)

Start with the simplest possible case. Write down what Genetic improvement (computer science) 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 Genetic improvement (computer science) 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 Genetic improvement (computer science) 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 Genetic improvement (computer science)

In research: Genetic improvement (computer science) 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 Genetic improvement (computer science) 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: Genetic improvement (computer science) is common in secondary-school and first-year university syllabi. It links to neighbouring topics Optimization algorithms and methods, so understanding it makes those chapters shorter.
In everyday life: Look for Genetic improvement (computer science) 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 Genetic improvement (computer science) in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Genetic improvement (computer science) 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 Genetic improvement (computer science) out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Genetic improvement (computer science) in simple terms?

Why does Genetic improvement (computer science) 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 Genetic improvement (computer science)?

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 Genetic improvement (computer science).