# Analysis of Selection, Mutation and Recombination in Genetic Algorithms

@inproceedings{Mhlenbein1995AnalysisOS, title={Analysis of Selection, Mutation and Recombination in Genetic Algorithms}, author={Heinz M{\"u}hlenbein and Dirk Schlierkamp-Voosen}, booktitle={Evolution and Biocomputation}, year={1995} }

Genetic algorithms have been applied fairly successful to a number of optimization problems. Nevertheless, a common theory why and when they work is still missing. In this paper a theory is outlined which is based on the science of plant and animal breeding. A central part of the theory is the response to selection equation and the concept of heritability. A fundamental theorem states that the heritability is equal to the regression coefficient of parent to offspring. The theory is applied to…

## 91 Citations

The Science of Breeding and Its Application to the Breeder Genetic Algorithm (BGA)

- Computer ScienceEvolutionary Computation
- 1993

It is shown how the response to selection equation and the concept of heritability can be applied to predict the behavior of the BGA and it is shown that recombination and mutation are complementary search operators.

An Individually Variable Mutation-Rate Strategy for Genetic Algorithms

- Computer ScienceEvolutionary Programming
- 1997

A mutation-rate strategy that is variable between individuals within a given generation based on the individual's relative performance for the purpose of function optimization is proposed.

On the practical usage of genetic algorithms in ecology and evolution

- Computer Science
- 2012

While genetic algorithms offer great power and flexibility by drawing inspiration from evolutionary processes, they are (usually) not a faithful model of genetics or evolution.

1 Statistical Inference as a Theoretical Foundation of Genetic Algorithms

- Computer Science

The aim of the theory is to identify measures which allow to control the BGA search most effectively, which should lead to a scientific foundation of one of the key technologies in real world computing-problem solving by simulating evolution.

On the practical usage of genetic algorithms in ecology and evolution

- Computer Science
- 2013

This article aims to demystify genetic algorithms and provide assistance to researchers; basic programming knowledge is important for working with genetic algorithms, but none is required to read this article.

On mutation and crossover in the theory of evolutionary algorithms

- Computer Science
- 2010

This dissertation describes a series of theoretical and experimental studies on a variety of evolutionary algorithms and models of those algorithms to explore the effects of parameterized mutation and crossover.

Recent developments in evolutionary and genetic algorithms: theory and applications

- Computer Science
- 1997

While the paper covers many works on the theory and application of genetic algorithms, not much details are reported on genetic programming, parallel Genetic algorithms, in addition to more advanced techniques e.g. micro-genetic algorithms and multiobjective optimisation.

Adapting Operator Settings in Genetic Algorithms

- BusinessEvolutionary Computation
- 1998

The results obtained indicate that the applicability of operator adaptation is dependent upon three basic assumptions being satisfied by the problem being tackled, including the ability of the operators to produce children of increased fitness.

Sexual Selection for Genetic Algorithms

- BusinessArtificial Intelligence Review
- 2004

A new selection scheme inspired by sexual selection principles through female choice selection is proposed, and the performance of this new schemewith commonly used selection methods in solvingsome well-known problems including the Royal Road problem, the Open Shop Scheduling Problem and the Job Shop Scheduled Problem are compared.

Empirical Modelling of Genetic Algorithms

- BusinessEvolutionary Computation
- 2001

This paper addresses the problem of reliably setting genetic algorithm parameters for consistent labelling problems by proposing a robust empirical framework, based on the analysis of factorial experiments, which are shown to be robust under extrapolation to up to triple the problem size.

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