# genetic algorithm

> competitive algorithm for searching a problem space

**Wikidata**: [Q187787](https://www.wikidata.org/wiki/Q187787)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Genetic_algorithm)  
**Source**: https://4ort.xyz/entity/genetic-algorithm

## Summary
A genetic algorithm is a competitive algorithm for searching a problem space, inspired by natural selection and evolution. It belongs to the class of evolutionary algorithms and is used for optimization problems across various fields including computer science, engineering, and mathematics.

## Key Facts
- Inception: 1975
- Instance of: algorithm
- Subclass of: informed search algorithm, evolutionary algorithm, machine learning method
- Aliases include: GA, algoritmo genetico, 遺伝アルゴリズム, 基因演算法, 优化问题
- Wikipedia languages: af, ar, az, bg, bn, ca, csk, cs, da, de
- Sitelink count: 52
- GND ID: 4265092-6
- PSH ID: 13628
- YSO ID: 7987
- Freebase ID: /m/0b10y
- Mesh Descriptor ID: D000098434
- Library of Congress Authority ID: sh92002377
- Dewey Decimal Classification: 005.1, 519.62, 006.3823

## FAQs
### Q: What is a genetic algorithm used for?
A: Genetic algorithms are used for optimization problems, searching through large solution spaces to find optimal or near-optimal solutions. They're applied in fields like engineering design, scheduling, machine learning, and data mining.

### Q: How does a genetic algorithm work?
A: Genetic algorithms work by maintaining a population of candidate solutions that evolve over generations through selection, crossover (recombination), and mutation operations, mimicking biological evolution to improve solution quality.

### Q: Who created genetic algorithms?
A: Genetic algorithms were developed in the 1960s and 1970s, with John Holland being credited as a key pioneer in their development at the University of Michigan.

## Why It Matters
Genetic algorithms represent a fundamental approach to solving complex optimization problems that are difficult or impossible to solve using traditional analytical methods. They provide a robust framework for finding solutions in vast, poorly understood search spaces where the relationship between variables is nonlinear or the solution landscape contains many local optima. Their evolutionary approach allows them to escape local optima and explore diverse regions of the solution space, making them particularly valuable for real-world problems in engineering design, scheduling, financial modeling, and machine learning. The algorithm's ability to handle multi-objective optimization and work with incomplete or noisy data has made it an essential tool in modern computational problem-solving, influencing fields from bioinformatics to robotics and beyond.

## Notable For
- Being one of the most successful and widely-used evolutionary computation techniques
- Providing solutions to problems that are intractable for traditional optimization methods
- Inspiring numerous variants and hybrid approaches combining evolutionary principles with other computational techniques
- Having applications across diverse fields from engineering to finance to biology
- Being a foundational concept in the field of evolutionary computation and artificial intelligence

## Body
### Classification and Relationships
Genetic algorithms are classified as a type of informed search algorithm, meaning they use heuristic information to guide the search process. They belong to the broader class of evolutionary algorithms, which simulate biological evolution to solve computational problems. As a subclass of machine learning methods, genetic algorithms are used for both supervised and unsupervised learning tasks.

### Technical Implementation
The algorithm operates by maintaining a population of candidate solutions, each represented as a chromosome (typically a string of bits or numbers). Through iterative processes of selection (choosing the fittest individuals), crossover (combining parts of two parents to create offspring), and mutation (randomly altering genes), the population evolves toward better solutions over successive generations.

### Applications and Variants
Genetic algorithms are used in scheduling problems (genetic algorithm scheduling), optimization tasks, and as part of interactive evolutionary computation where human evaluation guides the selection process. They're related to techniques like stochastic universal sampling for selection and hill climbing for local optimization. The algorithm has inspired numerous variants including genetic programming, evolution strategies, and evolutionary programming.

### Academic and Research Context
The field has produced extensive research with notable contributors including John Holland, David E. Goldberg, and Kalyanmoy Deb. Research continues in areas like multi-objective optimization, constraint handling, and hybridization with other computational intelligence techniques. The algorithm is studied across disciplines including computer science, mathematics, engineering, and biology.

### Performance Characteristics
Genetic algorithms are particularly effective for problems with large, complex search spaces where traditional gradient-based methods fail. They can handle discrete, continuous, and mixed-variable problems, and are robust to noise and incomplete information. However, they can be computationally expensive and may require careful parameter tuning for optimal performance.

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## References

1. Nuovo soggettario
2. Freebase Data Dumps. 2013
3. Integrated Authority File
4. YSO-Wikidata mapping project
5. [Source](https://golden.com/wiki/Genetic_algorithm-4NK34)
6. National Library of Israel
7. [genetic-algorithm · GitHub Topics · GitHub](https://github.com/topics/genetic-algorithm)
8. [OpenAlex](https://docs.openalex.org/download-snapshot/snapshot-data-format)