ArticleslgStudy

biology

Schema (genetic algorithms)

Schema (genetic algorithms) 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 Schema (genetic algorithms) rather than just read about it. In short: A schema (pl.: schemata) is a template in computer science used in the field of genetic algorithms that identifies a subset of strings with similarities at certain string positions. Schemata are a special case of cylinder sets, forming a basis for a product topology on strings.

Schema (genetic algorithms) — main illustration
Schema (genetic algorithms) — illustration

Key takeaways

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

Reference excerpt

A schema (pl.: schemata) is a template in computer science used in the field of genetic algorithms that identifies a subset of strings with similarities at certain string positions. Schemata are a special case of cylinder sets, forming a basis for a product topology on strings. In other words, schemata can be used to generate a topology on a space of strings.

Description For example, consider binary strings of length 6. The schema 1**0*1 describes the set of all words of length 6 with 1's at the first and sixth positions and a 0 at the fourth position. The * is a wildcard symbol, which means that positions 2, 3 and 5 can have a value of either 1 or 0. The order of a schema is defined as the number of fixed positions in the template, while the defining length δ ( H ) {\displaystyle \delta (H)} is the distance between the first and last specific positions. The order of 1**0*1 is 3 and its defining length is 5. The fitness of a schema is the average fitness of all strings matching the schema. The fitness of a string is the numerical score that tells how good the solution it represents is, calculated using a rule designed for the specific problem.

Length The length of a schema H {\displaystyle H} , called N ( H ) {\displaystyle N(H)} , is defined as the total number of nodes in the schema. N ( H ) {\displaystyle N(H)} is also equal to the number of nodes in the programs matching H {\displaystyle H} .

Disruption If the child of an individual that matches schema H does not itself match H, the schema is said to have been disrupted.

Propagation of schema In evolutionary computing such as genetic algorithms and genetic programming, propagation refers to the inheritance of characteristics of one generation by the next. For example, a schema is propagated if individuals in the current generation match it and so do those in the next generation. Those in the next generation may be (but do not have to be) children of parents who matched it.

The Expansion and Compression Operators Recently schema have been studied using order theory. Two basic operators are defined for schema: expansion and compression. The expansion maps a schema onto a set of words which it represents, while the compression maps a set of words on to a schema. In the following definitions Σ {\displaystyle \Sigma } denotes an alphabet, Σ l {\displaystyle \Sigma ^{l}} denotes all words of length l {\displaystyle l} over the alphabet Σ {\displaystyle \Sigma } , Σ ∗ {\displaystyle \Sigma _{*}} denotes the alphabet Σ {\displaystyle \Sigma } with the extra symbol ∗ {\displaystyle *} . Σ ∗ l {\displaystyle \Sigma _{*}^{l}} denotes all schema of length l {\displaystyle l} over the alphabet Σ ∗ {\displaystyle \Sigma _{*}} as well as the empty schema ϵ ∗ {\displaystyle \epsilon _{*}} . For any schema s ∈ Σ ∗ l {\displaystyle s\in \Sigma _{*}^{l}} the following operator ↑ s {\displaystyle {\uparrow }s} , called the e x p a n s i o n {\displaystyle expansion} of s {\displaystyle s} , which maps s {\displaystyle s} to a subset of words in Σ l {\displaystyle \Sigma ^{l}} :

↑ s := { b ∈ Σ l | b i = s i or s i = ∗ for each i ∈ { 1 , . . . , l } } {\displaystyle {\uparrow }s:=\{b\in \Sigma ^{l}|b_{i}=s_{i}{\mbox{ or }}s_{i}=*{\mbox{ for each }}i\in \{1,...,l\}\}}

… excerpt ends here. Continue reading the full article.

Illustrations

Schema (genetic algorithms) illustration
Schema (genetic algorithms): The Schematic lattice formed from the schematic completion on the set 
  
    
      
        A
        =
        {
        111
        ,
        011
        ,
        001
        }
      
    
    {\displaystyle A=\{111,011,001\}}
  
. Here the schematic lattice 
  
    
      
        (
        
          
            S
          
        
        (
        A
        )
        ,
        ≤
        )
      
    
    {\displaystyle ({\mathcal {S}}(A),\leq )}
  
 is shown as a Hasse diagram.
The Schematic lattice formed from the schematic completion on the set A = { 111 , 011 , 001 } {\displaystyle A=\{111,011,001\}} . Here the schematic lattice ( S ( A ) , ≤ ) {\displaystyle ({\mathcal {S}}(A),\leq )} is shown as a Hasse diagram.

Worked examples

Example 1 — a first encounter with Schema (genetic algorithms)

Start with the simplest possible case. Write down what Schema (genetic algorithms) 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 Schema (genetic algorithms) 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 Schema (genetic algorithms) 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 Schema (genetic algorithms)

In research
Schema (genetic algorithms) 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 Schema (genetic algorithms) 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
Schema (genetic algorithms) is common in secondary-school and first-year university syllabi. It links to neighbouring topics Genetic algorithms, so understanding it makes those chapters shorter.
In everyday life
Look for Schema (genetic algorithms) 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 “Schema (genetic algorithms)” →

Affiliate

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

How to study Schema (genetic algorithms) in 20 minutes

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

Frequently asked questions

What is Schema (genetic algorithms) in simple terms?

A schema (pl.: schemata) is a template in computer science used in the field of genetic algorithms that identifies a subset of strings with similarities at certain string positions. Schemata are a special case of cylinder sets, forming a basis for a product topology on strings.

Why does Schema (genetic algorithms) 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 Schema (genetic algorithms)?

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 Schema (genetic algorithms).

Tags

  • Genetic algorithms

Keep exploring