Mathematical Software - Genetic Algorithms - Tutorial

Genetic Algorithms - Introduction

Genetic algorithms (GA) can be divided into two main groups of haploid and diploid type. Both genetic models deal with populations of sequences of bits. Each sequence represents parameter or set of parameters written in

Information from that genotype is decoded to a numeric value (values) that is (are) called a phenotype. That decoding is often done according to commonly known representation of an integer number as a sequence of bits. Having this done the obtained numeric value (phenotype) is used to compute desired function according to a mathematical formula determining problem to solve. One-parametric problems are represented by scalar functions whereas more complicated multi-parametric ones are described by vector functions. In the first case, on the basis of a scalar representation of a system a corresponding fitness function is computed. In the second one, the Pareto approach is often applied (to learn more see The Pareto Condition.)

The fitness function is the essential measure of chromosome strength to occur in the next generation. The higher fitness of chromosome the greater chance to reproduce. Reproduction in population is thought as a randomized process based on the fitness function. Only these chromosomes that are selected can take part in producing the next generation. In haploid models, a pair of chromosomes is a pair of parents that exchange their genetic information through the crossover process whereas, in diploid models, the process of gametogenesis uses the crossover mechanism to exchange genetic information between gametes. In spite of that small difference between haploid and diploid models in both cases it is demanded to find the crossover point or points (in models with multiply crossovers) on two parent chromosomes with assumed crossover probability. Apart from that random process genetic information can be modified through mutations. They also are random and occur with prescribed mutation probability. To enhance genetic variability of newly created population also genetic reconfigurations can be applied to chromosomes.

Genetic Algorithms - Haploidy vs. Diploidy

Haploid genotypes (HG) are built from single chromosomes whereas diploid ones (DG) from a pair of chromosomes. Making genetic material double results in enriching genetic expression process of dominance while a single chromosome is directly decoded to the phenotype.

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Genetic Algorithms - Diploid Model - Dominance

Feature of dominance in a simple diallelic genotypes can be set as

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Diploid AG can be enlarged to triallelic genotypes where the bit of 1 value occurs in two variants i. e. of the weaker 1R and the stronger 1D type. Then a dominance scheme is shown in the table below

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Genetic Algorithms - Fitness Function

A fitness function is a non-negative criterion of quality computed for each individual in population. The better fitness the higher value of the fitness function. This feature of a genetic genotype can be hired to find the optimum solution (numeric value) for some mathematical problems like searching for maximum or minimum of a function. To do so, it is necessary to define a problem to solve as a f(x) function being the aim function. Let us consider two cases:

Genetic Algorithms - Constraints - Punishment Function

Punishment function H is a penalty added/subtracted to/from the aim function f(x) when any of constraints is violated. Consider two cases:

Genetic Algorithms - Fitness Function - Scaling

Scaling of the fitness function allows to modify a survival ability of a chromosome in population. It is often done for two reasons. The first one is to strengthen reproduction of weaker genotypes in first generations. In that way population slower tends to the optimum and longer constitute more flexible genetic population. The second reason is to make greater differences between very similar genotypes in last generations when the average fitness of whole population is very close to the sought solution. This improves the resolution of the method. The following types of the fitness function scaling are used:

Genetic Algorithms - Fitness Function - Sharing Function

A sharing function is a function of a distance between chromosomes in a population. This distance between chromosomes can be computed on the basis of their phenotypes or genotypes. When phenotypes are a measure of chromosomes similarity the distance function is given by the formula

dij = |xi - xj|

where xi and xj are phenotypes of i-th and j-th chromosome, respectively. Otherwise, when the computed distance reflects similarity between two genotypes the Hamming measure is used. It is defined as the number of different bits on corresponding sites on both chromosomes of equal length divided by the length of the first or the second genotype. The sharing function S(d) can be set as a linear or a power function of a distance dij between chromosomes i. e.

S(dij) = 1 - dijp

where the power p is a parameter in a genetic algorithm. The sharing function is computed in order to modify the fitness function Fi of i-th chromosome in the way:

Fi, sharing = Fi/ S(dij)

where the sum is over j.

Applying a sharing function to the genetic algorithm leads to splitting a population into a set of sub-populations. Thus the sharing function approach can be done to simulate species creation in genetic populations. For that reason, in mathematical optimization problems, it is used in searching for optimum of multi-modal functions.

Genetic Algorithms - Fitness Function - Selection

Having all population built with fitness factors computed for every chromosome in population the group of parents of next generation can be chosen. A selection mechanism is based on the one main rule the better fitness the greater reproduction (more children). It can be done by one of the following methods:

In each case, the selection is done up to complete a new population.

Genetic Algorithms - Reproduction - Crossover

Crossover is a process applied to a pair of chromosomes. As the first step the crossover point is randomly chosen with assumed probability. That point defines a position of dividing bit on both chromosomes. Bits taken before the crossover point from the first chromosome are merged with bits from the second one lying on and after that point. In that way a new genotype is created. The second genotype from the new pair is formed equivalently. In the example shown below cross point is 5.

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Genetic Algorithms - Reproduction - Mutations

Mutations are applied to each new chromosome with prescribed probability. If mutation occurs a bit value is changed to its opposite charge i. e. 1 → 0 and 0 → 1 in diallelic models whereas in triallelic ones this change is like this one: 1R → 1D or 1R → 0.

Genetic Algorithms - Reproduction - Reconfigurations

Reconfigurations are applied to chromosomes having more than one gene. In such cases allele location (locus) on a chromosome is not constant. It can vary depending on reconfigurations that occurred. The list below presents main types of reconfigurations

Genetic Algorithms - Pareto Approach

The Pareto condition says that a vector x is lower (partially) than a vector y when the following condition is fulfilled

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where xi and yi are i-th components of vectors x and y, respectively. These vectors may represent fitness vectors F belonging to different chromosomes in a population. Then problem of finding better solutions on the basis of higher value of scalar fitness function is replaced by searching for a group of chromosomes fulfilling the above-written condition. The Pareto condition, due to the way it is formulated, can be naturally applied to find a function minimum. When opposite problem is put forward a fitness vector should be multiplied by -1. Then above-written condition can be used straightforward.
That part of chromosomes that satisfies the Pareto condition has the same power to reproduce which results in the same rang value in selection routine. Having selected the first group of Pareto-positive chromosomes the next one can be chosen in the same manner within a sub-population of Pareto-rejected chromosomes. The group of genotypes has assigned successive rang value, obviously. The lower rang the greater number of children associated with. That procedure continues up to finding rang values for all chromosomes in population.

To obtain better results one can associate the multi-parameter Pareto-based problems with a technique of species forming (e.g. using the sharing function approach).

Genetic Algorithms - OptFinder

Package for genetic search - OptFinder.
To test this software partially, go to Online Optfinder Page.

Genetic Algorithms & Machine Learning

Genetic algorithms (GA) play also an important role in learning systems called the Genetic Based Machine Learning Systems. One type of these systems is called the Classifier System.

Classifier systems are systems that learn some rules to act in given circumstances. They are built from the following sub-systems

To learn more go to machine learning page.

Genetic Algorithms - References

  1. ^ David E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Publishing Company, Inc. 1989
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