# heuristic

> type of algorithm that may sometimes fail or produce an approximate, incorrect, or suboptimal result

**Wikidata**: [Q1981968](https://www.wikidata.org/wiki/Q1981968)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Heuristic_(computer_science))  
**Source**: https://4ort.xyz/entity/heuristic

## Summary
A heuristic is a type of algorithm that may sometimes fail or produce an approximate, incorrect, or suboptimal result. It is commonly used in artificial intelligence and computer science to find solutions quickly when exact methods are too slow or impractical. Heuristics trade guaranteed correctness for speed and efficiency.

## Key Facts
- A heuristic is a type of algorithm that may sometimes fail or produce an approximate, incorrect, or suboptimal result.
- Heuristics are a subclass of algorithms and are also classified under artificial intelligence.
- The opposite of a heuristic is an exact algorithm.
- Heuristics are partially coincident with randomized algorithms.
- The term "heuristic" has aliases including "computer heuristic," "heuristic algorithm," and "heuristic programming."
- Heuristics are used in informed search algorithms, where preliminary information (generally heuristic) is available.
- Metaheuristics are higher-level procedures designed to find, generate, or select a heuristic.
- The concept is documented in multiple languages including English, French, Italian, and Russian.

## FAQs
### Q: What is a heuristic in computer science?
A: A heuristic is a type of algorithm that may sometimes fail or produce an approximate, incorrect, or suboptimal result, often used when exact solutions are too slow or impractical.

### Q: How does a heuristic differ from an exact algorithm?
A: A heuristic trades guaranteed correctness for speed and efficiency, while an exact algorithm always produces the correct result but may be slower or more resource-intensive.

### Q: Where are heuristics commonly used?
A: Heuristics are commonly used in artificial intelligence, informed search algorithms, and optimization problems where finding a perfect solution is impractical.

## Why It Matters
Heuristics are essential in computer science and artificial intelligence because they enable systems to solve complex problems efficiently when exact solutions are computationally infeasible. By accepting the possibility of approximate or suboptimal results, heuristics allow for rapid decision-making and problem-solving in real-world applications such as pathfinding, scheduling, and data analysis. They form the backbone of many practical algorithms that power modern technology, from search engines to route planning. Without heuristics, many of today's computational tasks would be too slow or resource-intensive to be useful.

## Notable For
- Trading correctness for speed in problem-solving
- Being a fundamental concept in artificial intelligence and computer science
- Enabling practical solutions to otherwise intractable computational problems
- Serving as the basis for metaheuristic approaches to optimization
- Being documented across multiple languages and cultures in computer science

## Body
### Definition and Purpose
A heuristic is fundamentally a problem-solving approach that uses practical methods or shortcuts to produce solutions that may not be perfect but are sufficient for immediate goals. The core characteristic is that heuristics may sometimes fail or produce approximate, incorrect, or suboptimal results.

### Relationship to Other Concepts
Heuristics are classified as a type of algorithm and are also considered part of the artificial intelligence field. They are the opposite of exact algorithms, which guarantee correct results. Heuristics are partially coincident with randomized algorithms, sharing some characteristics but maintaining distinct purposes.

### Applications
Heuristics are widely used in informed search algorithms, where preliminary information guides the search process. They are also fundamental to metaheuristic approaches, which are higher-level procedures designed to find, generate, or select appropriate heuristics for specific problems.

### Classification
The concept of heuristics spans multiple classification systems, including being categorized under "Technology--Technology (General)--Industrial engineering--Applied mathematics" in library classifications. It has specific identifiers in medical and scientific databases, reflecting its importance across disciplines.

### Cultural and Linguistic Reach
The concept of heuristics has been translated and documented in numerous languages, including Arabic, Catalan, Czech, English, Persian, French, Hebrew, Indonesian, Italian, and Macedonian, demonstrating its universal importance in computer science.

## Schema Markup
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  "name": "heuristic",
  "description": "type of algorithm that may sometimes fail or produce an approximate, incorrect, or suboptimal result",
  "url": "https://en.wikipedia.org/wiki/Heuristic_(computer_science)",
  "sameAs": [
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## References

1. Medical Subject Headings
2. Freebase Data Dumps. 2013
3. UMLS 2023