Cultural algorithm

A Cultural Algorithm is a type of evolutionary algorithm that incorporates the concept of culture to guide the evolutionary process. It was first introduced by Robert G. Reynolds in 1994. Cultural algorithms are inspired by the dual inheritance theory, which suggests that human evolution is influenced by both genetic and cultural factors.

Components of Cultural Algorithms

Cultural algorithms consist of two main components:

• Population Space: This is similar to the population in traditional evolutionary algorithms. It consists of a set of individuals, each representing a potential solution to the problem at hand.

• Belief Space: This is a unique feature of cultural algorithms. The belief space represents the cultural knowledge that is shared among the individuals in the population. It is used to influence the evolution of the population by guiding the selection and variation processes.

Process of Cultural Algorithms

The process of a cultural algorithm involves the following steps:

1. **Initialization**: The population space is initialized with a set of random individuals, and the belief space is initialized with some initial cultural knowledge.

2. **Evaluation**: Each individual in the population is evaluated based on a predefined fitness function.

3. **Update Belief Space**: The belief space is updated based on the performance of the individuals. This involves extracting useful information from the best-performing individuals and incorporating it into the belief space.

4. **Influence Population**: The belief space influences the population by guiding the selection of individuals for reproduction and the application of variation operators such as crossover and mutation.

5. **Reproduction**: New individuals are generated through the application of variation operators, influenced by the belief space.

6. **Termination**: The process repeats until a termination condition is met, such as a maximum number of generations or a satisfactory fitness level.

Applications

Cultural algorithms have been applied to a variety of optimization and search problems. They are particularly useful in complex problem domains where cultural knowledge can provide significant guidance to the search process.

See Also

• Genetic algorithm
• Memetic algorithm
• Swarm intelligence

References

• Reynolds, R. G. (1994). "An introduction to cultural algorithms". In Proceedings of the Third Annual Conference on Evolutionary Programming, pp. 131-139.

External Links

• [Cultural Algorithms on Scholarpedia](http://www.scholarpedia.org/article/Cultural_algorithms)