biology

Interactive evolutionary computation

Interactive evolutionary computation is a biology 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 Interactive evolutionary computation rather than just read about it. In short: Interactive evolutionary computation (IEC) or aesthetic selection is a general term for methods of evolutionary computation that use human evaluation. Usually human evaluation is necessary when the form of fitness function is not known (for example, visual appeal or attractiveness; as in Dawkins, 1986) or the result of optimization should fit a particular user preference (for example, taste of coffee or color set of…

Key takeaways

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

Reference excerpt

Interactive evolutionary computation (IEC) or aesthetic selection is a general term for methods of evolutionary computation that use human evaluation. Usually human evaluation is necessary when the form of fitness function is not known (for example, visual appeal or attractiveness; as in Dawkins, 1986) or the result of optimization should fit a particular user preference (for example, taste of coffee or color set of the user interface).

IEC design issues The number of evaluations that IEC can receive from one human user is limited by user fatigue which was reported by many researchers as a major problem. In addition, human evaluations are slow and expensive as compared to fitness function computation. Hence, one-user IEC methods should be designed to converge using a small number of evaluations, which necessarily implies very small populations. Several methods were proposed by researchers to speed up convergence, like interactive constrain evolutionary search (user intervention) or fitting user preferences using a convex function. IEC human–computer interfaces should be carefully designed in order to reduce user fatigue. There is also evidence that the addition of computational agents can successfully counteract user fatigue. However IEC implementations that can concurrently accept evaluations from many users overcome the limitations described above. An example of this approach is an interactive media installation by Karl Sims that allows one to accept preferences from many visitors by using floor sensors to evolve attractive 3D animated forms. Some of these multi-user IEC implementations serve as collaboration tools, for example HBGA.

IEC types IEC methods include interactive evolution strategy, interactive genetic algorithm, interactive genetic programming, and human-based genetic algorithm.

IGA An interactive genetic algorithm (IGA) is defined as a genetic algorithm that uses human evaluation. These algorithms belong to a more general category of Interactive evolutionary computation. The main application of these techniques include domains where it is hard or impossible to design a computational fitness function, for example, evolving images, music, various artistic designs and forms to fit a user's aesthetic preferences. Interactive computation methods can use different representations, both linear (as in traditional genetic algorithms) and tree-like ones (as in genetic programming).

See also Evolutionary art Human-based evolutionary computation Human-based genetic algorithm Human–computer interaction Karl Sims Electric Sheep User review

References

Banzhaf, W. (1997), Interactive Evolution, Entry C2.9, in: Handbook of Evolutionary Computation, Oxford University Press, ISBN 978-0750308953

External links "EndlessForms.com, Collaborative interactive evolution allowing you to evolve 3D objects and have them 3D printed". Archived from the original on 2018-11-14. Retrieved 2011-06-18. "Art by Evolution on the Web Interactive Art Generator". Archived from the original on 2018-04-15. Retrieved 2010-04-09. "Facial composite system using interactive genetic algorithms". "Galapagos by Karl Sims". "E-volver". "SBART, a program to evolve 2D images". "GenJam (Genetic Jammer)". "Evolutionary music". "Darwin poetry". Archived from the original on 2006-04-12. "Takagi Lab at Kyushu University". "Interactive one-max problem allows to compare the performance of interactive and human-based genetic algorithms". Archived from the original on 2011-07-09. Retrieved 2006-12-03.. "Webpage that uses interactive evolutionary computation with a generative design algorithm to generate 2d images". "Picbreeder service, Collaborative interactive evolution allowing branching from other users' creations that produces pictures like faces and spaceships". Archived from the original on 2011-07-25. Retrieved 2007-08-02. "Peer to Peer IGA Using collaborative IGA sessions for floorplanning and document design".

Worked examples

Example 1 — a first encounter with Interactive evolutionary computation

Start with the simplest possible case. Write down what Interactive evolutionary computation claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In biology, 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 Interactive evolutionary computation 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 Interactive evolutionary computation 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 Interactive evolutionary computation

In research: Interactive evolutionary computation appears in biology 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 Interactive evolutionary computation 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: Interactive evolutionary computation is common in secondary-school and first-year university syllabi. It links to neighbouring topics Evolutionary computation, Interactive evolutionary computation, so understanding it makes those chapters shorter.
In everyday life: Look for Interactive evolutionary computation 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 Interactive evolutionary computation in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Interactive evolutionary computation 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 Interactive evolutionary computation out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Interactive evolutionary computation in simple terms?

Why does Interactive evolutionary computation matter?

Because it connects several biology 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 Interactive evolutionary computation?

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 Interactive evolutionary computation.