computer science

Human-based evolutionary computation

Human-based evolutionary computation 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 Human-based evolutionary computation rather than just read about it. In short: Human-based evolutionary computation (HBEC) is a set of evolutionary computation techniques that rely on human innovation. Classes and examples Human-based evolutionary computation techniques can be classified into three more specific classes analogous to ones in evolutionary computation.

Key takeaways

Human-based evolutionary computation 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 Human-based evolutionary computation to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Human-based evolutionary computation from memory before moving on to harder problems.

Reference excerpt

Human-based evolutionary computation (HBEC) is a set of evolutionary computation techniques that rely on human innovation.

Classes and examples Human-based evolutionary computation techniques can be classified into three more specific classes analogous to ones in evolutionary computation. There are three basic types of innovation: initialization, mutation, and recombination. Here is a table illustrating which type of human innovation are supported in different classes of HBEC:

All these three classes also have to implement selection, performed either by humans or by computers.

Human-based selection strategy Human-based selection strategy is a simplest human-based evolutionary computation procedure. It is used heavily today by websites outsourcing collection and selection of the content to humans (user-contributed content). Viewed as evolutionary computation, their mechanism supports two operations: initialization (when a user adds a new item) and selection (when a user expresses preference among items). The website software aggregates the preferences to compute the fitness of items so that it can promote the fittest items and discard the worst ones. Several methods of human-based selection were analytically compared in studies by Kosorukoff and Gentry. Because the concept seems too simple, most of the websites implementing the idea can't avoid the common pitfall: informational cascade in soliciting human preference. For example, digg-style implementations, pervasive on the web, heavily bias subsequent human evaluations by prior ones by showing how many votes the items already have. This makes the aggregated evaluation depend on a very small initial sample of rarely independent evaluations. This encourages many people to game the system that might add to digg's popularity but detract from the quality of the featured results. It is too easy to submit evaluation in digg-style system based only on the content title, without reading the actual content supposed to be evaluated. A better example of a human-based selection system is StumbleUpon. In StumbleUpon, users first experience the content (stumble upon it), and can then submit their preference by pressing a thumb-up or thumb-down button. Because the user doesn't see the number of votes given to the site by previous users, StumbleUpon can collect a relatively unbiased set of user preferences, and thus evaluate content much more precisely.

Human-based evolution strategy In this context and maybe generally, the Wikipedia software is the best illustration of a working human-based evolution strategy wherein the (targeted) evolution of any given page comprises the fine tuning of the knowledge base of such information that relates to that page. Traditional evolution strategy has three operators: initialization, mutation, and selection. In the case of Wikipedia, the initialization operator is page creation, the mutation operator is incremental page editing. The selection operator is less salient. It is provided by the revision history and the ability to select among all previous revisions via a revert operation. If the page is vandalised and no longer a good fit to its title, a reader can easily go to the revision history and select one of the previous revisions that fits best (hopefully, the previous one). This selection feature is crucial to the success of the Wikipedia. An interesting fact is that the original wiki software was created in 1995, but it took at least another six years for large wiki-based collaborative projects to appear. Why did it take so long? One explanation is that the original wiki software lacked a selection operation and hence couldn't effectively support content evolution. The addition of revision history and the rise of large wiki-supported communities coincide in time. From an evolutionary computation point of view, this is not surprising: without a selection operation the content would undergo an aimless genetic drift and would unlikely to be useful to anyone. That is what many people expected from Wikipedia at its inception. However, with a selection operation, the utility of content has a tendency to improve over time as beneficial changes accumulate. This is what actually happens on a large scale in Wikipedia.

Human-based genetic algorithm

Human-based genetic algorithm (HBGA) provides means for human-based recombination operation (a distinctive feature of genetic algorithms). Recombination operator brings together highly fit parts of different solutions that evolved independently. This makes the evolutionary process more efficient.

See also Incrementalism – Adding to a project via many small changes instead of fewer large changes Interactive evolutionary computation

References

Worked examples

Example 1 — a first encounter with Human-based evolutionary computation

Start with the simplest possible case. Write down what Human-based evolutionary computation 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 Human-based 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 Human-based 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 Human-based evolutionary computation

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

Ask Teacher Smith questions about this articleOpens your AI tutor with a question about “Human-based evolutionary computation” →

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Human-based evolutionary computation in 20 minutes

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

Frequently asked questions

What is Human-based evolutionary computation in simple terms?

Human-based evolutionary computation (HBEC) is a set of evolutionary computation techniques that rely on human innovation. Classes and examples Human-based evolutionary computation techniques can be classified into three more specific classes analogous to ones in evolutionary computation.

Why does Human-based evolutionary computation 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 Human-based 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 Human-based evolutionary computation.