biology

Human-based genetic algorithm

Human-based genetic algorithm 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 Human-based genetic algorithm rather than just read about it. In short: In evolutionary computation, a human-based genetic algorithm (HBGA) is a genetic algorithm that allows humans to contribute solution suggestions to the evolutionary process. For this purpose, a HBGA has human interfaces for initialization, mutation, and recombinant crossover.

Key takeaways

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

Reference excerpt

In evolutionary computation, a human-based genetic algorithm (HBGA) is a genetic algorithm that allows humans to contribute solution suggestions to the evolutionary process. For this purpose, a HBGA has human interfaces for initialization, mutation, and recombinant crossover. As well, it may have interfaces for selective evaluation. In short, a HBGA outsources the operations of a typical genetic algorithm to humans.

Evolutionary genetic systems and human agency Among evolutionary genetic systems, HBGA is the computer-based analogue of genetic engineering (Allan, 2005). This table compares systems on lines of human agency:

One obvious pattern in the table is the division between organic (top) and computer systems (bottom). Another is the vertical symmetry between autonomous systems (top and bottom) and human-interactive systems (middle). Looking to the right, the selector is the agent that decides fitness in the system. It determines which variations will reproduce and contribute to the next generation. In natural populations, and in genetic algorithms, these decisions are automatic; whereas in typical HBGA systems, they are made by people. The innovator is the agent of genetic change. The innovator mutates and recombines the genetic material, to produce the variations on which the selector operates. In most organic and computer-based systems (top and bottom), innovation is automatic, operating without human intervention. In HBGA, the innovators are people. HBGA is roughly similar to genetic engineering. In both systems, the innovators and selectors are people. The main difference lies in the genetic material they work with: electronic data vs. polynucleotide sequences.

Differences from a plain genetic algorithm All four genetic operators (initialization, mutation, crossover, and selection) can be delegated to humans using appropriate interfaces (Kosorukoff, 2001). Initialization is treated as an operator, rather than a phase of the algorithm. This allows a HBGA to start with an empty population. Initialization, mutation, and crossover operators form the group of innovation operators. Choice of genetic operator may be delegated to humans as well, so they are not forced to perform a particular operation at any given moment.

Functional features HBGA is a method of collaboration and knowledge exchange. It merges competence of its human users creating a kind of symbiotic human-machine intelligence (see also distributed artificial intelligence). Human innovation is facilitated by sampling solutions from population, associating and presenting them in different combinations to a user (see creativity techniques). HBGA facilitates consensus and decision making by integrating individual preferences of its users. HBGA makes use of a cumulative learning idea while solving a set of problems concurrently. This allows to achieve synergy because solutions can be generalized and reused among several problems. This also facilitates identification of new problems of interest and fair-share resource allocation among problems of different importance. The choice of genetic representation, a common problem of genetic algorithms, is greatly simplified in HBGA, since the algorithm need not be aware of the structure of each solution. In particular, HBGA allows natural language to be a valid representation. Storing and sampling population usually remains an algorithmic function. A HBGA is usually a multi-agent system, delegating genetic operations to multiple agents (humans).

Applications Evolutionary knowledge management, integration of knowledge from different sources. Social organization, collective decision-making, and e-governance. Traditional areas of application of interactive genetic algorithms: computer art, user-centered design, etc. Collaborative problem solving using natural language as a representation. Education and Academic benefits from Real Time Simulation with Synthetic Curriculum Modeling using Dynamic Point Cloud environments. The HBGA methodology was derived in 1999-2000 from analysis of the Free Knowledge Exchange project that was launched in the summer of 1998, in Russia (Kosorukoff, 1999). Human innovation and evaluation were used in support of collaborative problem solving. Users were also free to choose the next genetic operation to perform. Currently, several other projects implement the same model, the most popular being Yahoo! Answers, launched in December 2005. Recent research suggests that human-based innovation operators are advantageous not only where it is hard to design an efficient computational mutation and/or crossover (e.g. when evolving solutions in natural language), but also in the case where good computational innovation operators are readily available, e.g. when evolving an abstract picture or colors (Cheng and Kosorukoff, 2004). In the latter case, human and computational innovation can complement each other, producing cooperative results and improving general user experience by ensuring that spontaneous creativity of users will not be lost. Furthermore, human-based genetic algorithms prove to be a successful measure to counteract fatigue effects introduced by interactive genetic algorithms.

See also Human-based computation Human-based evolutionary computation Human–computer interaction Interactive genetic algorithm Memetics Social computing

References

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Worked examples

Example 1 — a first encounter with Human-based genetic algorithm

Start with the simplest possible case. Write down what Human-based genetic algorithm 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 Human-based genetic algorithm 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 genetic algorithm 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 genetic algorithm

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

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

Frequently asked questions

What is Human-based genetic algorithm in simple terms?

Why does Human-based genetic algorithm 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 Human-based genetic algorithm?

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 genetic algorithm.