Cultural algorithm Totally Explained

Everything about Cultural Algorithm totally explained

Cultural algorithms (CA) are a branch of evolutionary computation where there's a knowledge component that's called the belief space in addition to the population component. In this sense, cultural algorithms can be seen as an extension to a conventional genetic algorithm. Cultural algorithms were introduced by Reynolds (see references).

Belief space

The belief space of a cultural algorithm is divided into distinct categories. These categories represent different domains of knowledge that the population has of the search space.
The belief space is updated after each iteration by the best individuals of the population. The best individuals can selected using a fitness function that assesses the performance of each individual in population much like in genetic algorithms.

List of belief space categories

Normative knowledge A collection of desirable value ranges for the individuals in the population component eg. acceptable behavior for the agents in population.
Domain specific knowledge Information about the domain of the problem CA is applied to.
Situational knowledge Specific examples of important events - eg. succesful/unsuccesful solutions
Temporal knowledge History of the search space - eg. the temporal patterns of the search process
Spatial knowledge Information about the topography of the search space

Population

The population component of the cultural algorithm is approximately the same as that of the genetic algorithm.

Communication protocol

Cultural algorithms require an interface between the population and belief space. The best individuals of the population can update the belief space via the update function. In the other hand, the knowledge categories of the belief space can affect the population component via influence function. The influence function can affect population by altering the genome or the actions of the individuals.

Pseudo-code for cultural algorithms

Initialize population space (choose initial population)

Initialize belief space (eg. set domain specific knowledge and normative value-ranges)

Repeat until termination condition is met

Perform actions of the individuals in population space
Evaluate each individual by using the fitness function
Select the parents to reproduce a new generation of offspring
Let the belief space alter the genome of the offspring by using the influence function
Update the belief space by using the accept function (this is done by letting the best individuals to affect the belief space)

Applications

Various optimization problems

Social simulationFurther Information

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