Cultural algorithms (CA) are a branch of evolutionary computation where there is a knowledge component that is called the belief space in addition to the population component. In Computer science evolutionary computation is a subfield of Artificial intelligence (more particularly Computational intelligence) that involves In Biology a population is the collection of inter-breeding organisms of a particular Species; in Sociology In this sense, cultural algorithms can be seen as an extension to a conventional genetic algorithm. A genetic algorithm (GA is a Search technique used in Computing to find exact or Approximate solutions to optimization and Search Cultural algorithms were introduced by Reynolds (see references).

Contents 1 Belief space 1.1 List of belief space categories 2 Population 3 Communication protocol 4 Pseudo-code for cultural algorithms 5 Applications 6 See also 7 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. Iteration means the act of repeating Mathematics Iteration in mathematics may refer to the process of iterating a function, or to the techniques used The best individuals can selected using a fitness function that assesses the performance of each individual in population much like in genetic algorithms. A fitness function is a particular type of Objective function that quantifies the optimality of a solution (that is a chromosome) in a Genetic algorithm

List of belief space categories

• Normative knowledge A collection of desirable value ranges for the individuals in the population component eg. Normative has specialized meanings in several academic disciplines 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. A genetic algorithm (GA is a Search technique used in Computing to find exact or Approximate solutions to optimization and Search

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

1. Initialize population space (choose initial population)
2. Initialize belief space (eg. In Biology a population is the collection of inter-breeding organisms of a particular Species; in Sociology set domain specific knowledge and normative value-ranges)
3. Repeat until termination condition is met
   1. Perform actions of the individuals in population space
   2. Evaluate each individual by using the fitness function
   3. Select the parents to reproduce a new generation of offspring
   4. Let the belief space alter the genome of the offspring by using the influence function
   5. 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 simulation

See also

• Genetic algorithm
• Evolutionary computation
• Memetic algorithm
• Artificial intelligence
• Artificial life
• Machine learning
• Stochastic optimization
• Metaheuristic
• Swarm intelligence
• Harmony search
• Memetics
• Social simulation
• Sociocultural evolution

References

• Robert G. Reynolds, Ziad Kobti, Tim Kohler: Agent-Based Modeling of Cultural Change in Swarm Using Cultural Algorithms

Social simulation is the modeling or Simulation, normally performed using a computer, of social phenomena (e A genetic algorithm (GA is a Search technique used in Computing to find exact or Approximate solutions to optimization and Search In Computer science evolutionary computation is a subfield of Artificial intelligence (more particularly Computational intelligence) that involves Memetic algorithms (MA represent one of the recent growing areas of research in evolutionary computation Artificial life (commonly Alife or alife) is a field of study and an associated art form which examine Systems related to Life, its processes Machine learning is a subfield of Artificial intelligence that is concerned with the design and development of Algorithms and techniques that allow computers to "learn" Stochastic optimization (SO methods are optimization Algorithms which incorporate probabilistic (random elements either in the problem data (the A metaheuristic is a Heuristic method for solving a very general class of computational problems by combining user-given black-box procedures €”usually heuristics Swarm intelligence (SI is Artificial intelligence based on the Collective behavior of decentralized, self-organized systems Harmony search (HS is a Metaheuristic algorithm (also known as Soft computing algorithm or Evolutionary algorithm) mimicking the improvisation process This article is related to the study of self-replicating units of culture not to be confused with Mimetics Memetics is a neo-Darwinian approach Social simulation is the modeling or Simulation, normally performed using a computer, of social phenomena (e Sociocultural evolution(ism is an umbrella term for theories of cultural evolution and Social evolution, describing how Cultures and societies

---

© 2009 citizendia.org; parts available under the terms of GNU Free Documentation License, from http://en.wikipedia.org
Dapyx Software network: MP3 Explorer | Ebook Manager | Zenithic