# A genetic algorithm tutorial

@article{Whitley1994AGA, title={A genetic algorithm tutorial}, author={L. D. Whitley}, journal={Statistics and Computing}, year={1994}, volume={4}, pages={65-85} }

This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm.

## 3,466 Citations

### A Genetic Programming Tutorial

- Computer Science, Biology
- 2003

This chapter introduces the basics of genetic programming and touches upon some of the more advanced variants of genetic Programming as well as its theoretical foundations.

### An Introduction to Genetic Algorithms and Evolution

- Computer Science
- 2002

The history, theory and mathematical background, applications, and the current direction of both Genetic Algorithms and Evolution Strategies are examined.

### Genetic Algorithms

- Computer Science
- 2010

This article provides an introduction to genetic algorithms as well as numerous pointers for obtaining additional information.

### Genetic algorithms overview

- Computer Science
- 2000

This paper presents genetic algorithms, adaptive methods which may be used to solve search and optimisation problems, and the basic principles of GAs, first laid down rigorously by Holland.

### A New P System Based Genetic Algorithm

- Computer Science
- 2016

The new P system based genetic algorithm (PBGA), based on the parallel mechanism of P system in membrane computing, is put forward so that the performance of GA can improve.

### An overview of evolutionary algorithms: practical issues and common pitfalls

- Computer ScienceInf. Softw. Technol.
- 2001

### Genetic Algorithm

- Computer Science

Genetic Algorithms are a family of computational models inspired by evolution. These algorithms encode a potential solution to a specific problem on a simple chromosome-like data structure and apply…

### Foundations of Evolutionary Algorithms

- Computer ScienceStable Mutations for Evolutionary Algorithms
- 2018

Evolutionary algorithms are a broad class of stochastic adaptation algorithms inspired by biological evolution—the process that allows populations of organisms to adapt to their surrounding…

### Genetic optimization algorithms applied toward mission computability models

- Computer ScienceArXiv
- 2020

This paper describes the genetic optimization algorithms to a mission-critical and constraints-aware computation problem.

### Parallel Population Models for Genetic Algorithms

- Computer Science
- 1996

A flexible parallel population model for genetic algorithms is derived, which contains all the above models as a special case and could nevertheless be implemented on many parallel architectures.

## References

SHOWING 1-10 OF 59 REFERENCES

### Cellular Genetic Algorithms

- Computer ScienceICGA
- 1993

This chapter introduces the applications of cellular automata in genetic algorithms, which makes it especially suitable for dealing with complex and nonlinear problems which are difficult to be solved by general searching methods.

### Modeling Simple Genetic Algorithms

- Computer ScienceEvolutionary Computation
- 1995

The infinite- and finite-population models of the simple genetic algorithm are extended and unified, The result incorporates both transient and asymptotic GA behavior. This leads to an interpretation…

### A Survey of Evolution Strategies

- BiologyICGA
- 1991

Evolution Strategies are algorithms which imitate the principles of natural evolution as a method to solve parameter optimization problems and adaptation of the strategy parameters for the mutation variances as well as their covariances are described.

### Genetic Algorithms for Real Parameter Optimization

- Computer ScienceFOGA
- 1990

### Selection in Massively Parallel Genetic Algorithms

- Computer ScienceICGA
- 1991

This paper characterize the difference between panmictic and local selection/mating schemes in terms of diversity of alleles, diversity of genotypes, the inbreeding, and the speed and robustness of the genetic algorithm.

### Explicit Parallelism of Genetic Algorithms through Population Structures

- Computer SciencePPSN
- 1990

This paper specifies an algorithm which uses only local rules and local data making it massively parallel with an observed linear speedup on a transputer-based parallel system, and shows that both convergence speed and final quality are improved in comparison to a genetic algorithm without population structure.

### A Comparative Analysis of Selection Schemes Used in Genetic Algorithms

- Computer ScienceFOGA
- 1990

### A Study of Reproduction in Generational and Steady State Genetic Algorithms

- Computer ScienceFOGA
- 1990

### A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-Oriented Simulated Annealing

- Computer ScienceComplex Syst.
- 1990

In this note, the motivation, the theory of operation, some proof-of-principle computational experiments, and a Pascal implementation of the algorithm are presented.