# Stochastic universal sampling

> data sampling technique used in genetic algorithm

**Wikidata**: [Q11949983](https://www.wikidata.org/wiki/Q11949983)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Stochastic_universal_sampling)  
**Source**: https://4ort.xyz/entity/stochastic-universal-sampling

## Summary
Stochastic universal sampling (SUS) is a data sampling technique used in genetic algorithms to select individuals for reproduction with minimal bias. Developed by James E. Baker, it improves upon traditional fitness proportionate selection by ensuring a more uniform distribution of choices across the population. This method is particularly useful in optimization tasks where reducing selection bias is critical.

## Key Facts
- **Inventor**: James E. Baker, an American computer scientist.
- **Primary Use**: A sampling method in genetic algorithms to select individuals for reproduction.
- **Key Advantage**: Reduces bias compared to fitness proportionate selection by ensuring a single pass over the population.
- **Related Field**: Genetic algorithms (inception: 1975), a competitive algorithm for searching problem spaces.
- **Wikidata Properties**: Freebase ID `/m/027n960`, instance of "genetic algorithm," and 3 sitelinks.
- **Wikipedia Coverage**: Available in English, Bulgarian, and Catalan.

## FAQs
### Q: What problem does stochastic universal sampling solve?
A: It addresses selection bias in genetic algorithms by ensuring a more uniform distribution of selected individuals, improving optimization outcomes.

### Q: Who created stochastic universal sampling?
A: It was developed by James E. Baker, an American computer scientist and university teacher.

### Q: How does SUS differ from roulette wheel selection?
A: Unlike roulette wheel selection, SUS completes a single pass over the population, reducing computational redundancy and bias.

## Why It Matters
Stochastic universal sampling plays a pivotal role in genetic algorithms by refining the selection process, a core component of evolutionary optimization. By minimizing bias in parent selection, SUS enhances the algorithm's ability to explore the solution space effectively, leading to more robust and diverse solutions. This technique is particularly impactful in scenarios where traditional methods like roulette wheel selection might skew toward overly dominant individuals, risking premature convergence. As genetic algorithms are applied across fields such as engineering, economics, and artificial intelligence, SUS contributes to their reliability and efficiency, underscoring its importance in computational problem-solving.

## Notable For
- **Single-Pass Efficiency**: Completes selection in one pass over the population, reducing computational overhead.
- **Low-Bias Selection**: Mitigates bias more effectively than fitness proportionate methods like roulette wheel selection.
- **Inventor’s Contribution**: Developed by James E. Baker, a notable figure in computer science and cybersecurity.
- **Cross-Disciplinary Use**: Applied in optimization challenges across multiple domains due to its integration with genetic algorithms.

## Body
### Definition & Purpose
Stochastic universal sampling is a data sampling technique designed for genetic algorithms, a class of optimization methods inspired by natural selection. Its primary purpose is to select individuals for reproduction with reduced bias, ensuring a balanced representation of the population’s genetic diversity.

### Inventor
James E. Baker, an American computer scientist, developed SUS. Baker’s work spans multiple roles, including university teaching and contributions to cybersecurity, as evidenced by his appointment to a Cyber Security Board in 2017.

### Technical Details
- **Methodology**: SUS operates by making a single pass over the population, assigning selection probabilities based on normalized fitness values. This approach contrasts with roulette wheel selection, which may require multiple passes and introduce bias.
- **Integration**: The technique is embedded within the framework of genetic algorithms, which were first conceptualized in 1975. These algorithms are used to solve complex optimization problems by iteratively evolving candidate solutions.

### Properties
- **Wikidata**: SUS is classified as an instance of a genetic algorithm, with a freebase ID `/m/027n960` and coverage in three Wikipedia languages (English, Bulgarian, Catalan).
- **Academic Context**: While no specific inception date for SUS is provided, its association with genetic algorithms places it within a field that has grown significantly since the 1970s, supported by academic and practical applications.