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Culture and Arti cial Culture
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A number of de nitions of culture can be found, for example1 : Culture is a system of symbolically encoded conceptual phenomena that are socially and historically transmitted within and between social groups [223] Culture refers to the cumulative deposit of knowledge, experience, beliefs, values, attitudes, meanings, hierarchies, religion, notions of time, roles, spatial relations, concepts of the universe, and material objects and possessions acquired
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1 wwwtamuedu/classes/cosc/choudhury/culturehtml Computational Intelligence: An Introduction, Second Edition AP Engelbrecht c 2007 John Wiley & Sons, Ltd
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14 Cultural Algorithms by a group of people in the course of generations through individual and group striving
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Culture is the sum total of the learned behavior of a group of people that is generally considered to be the tradition of that people and is transmitted from generation to generation Culture is a collective programming of the mind that distinguishes the members of one group or category of people from another In terms of evolutionary computation, culture is modeled as the source of data that in uences the behavior of all individuals within that population This di ers from EP and ES where the behavioral characteristics of individuals for the current generation only are modeled using phenotypes Within cultural algorithms, culture stores the general behavioral traits of the population Cultural information is then accessible to all the individuals of a population, and over many generations
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Basic Cultural Algorithm
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A cultural algorithm (CA) is a dual-inheritance system, which maintains two search spaces: the population space (to represent a genetic component based on Darwinian principles), and a belief space (to represent a cultural component) It is the latter that distinguishes CAs from other EAs The belief space models the cultural information about the population, while the population space represents the individuals on a genotypic and/or phenotypic level Both the population and belief spaces evolve in parallel, with both in uencing one another A communication protocol therefore forms an integral part of a CA Such a protocol de nes two communication channels One for a select group of individuals to adapt the set of beliefs, and another de ning the way that the beliefs in uence all of the individuals in the population space A pseudocode cultural algorithm is given in Algorithm 141, and illustrated in Figure 141 Algorithm 141 Cultural Algorithm Set the generation counter, t = 0; Create and initialize the population space, C(0); Create and initialize the belief space, B(0); while stopping condition(s) not true do Evaluate the tness of each xi (t) C(t); Adjust (B(t), Accept (C(t))); Variate (C(t), In uence (B(t))); t = t + 1; Select the new population; end
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143 Belief Space
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Influence population
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Selection Variate population
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Figure 141 Illustration of Population and Belief Spaces of Cultural Algorithms At each iteration (each generation), individuals are rst evaluated using the tness function speci ed for the EA on the population level An acceptance function is then used to determine which individuals from the current population have an in uence on the current beliefs The experience of the accepted individuals is then used to adjust the beliefs (to simulate evolution of culture) The adjusted beliefs are then used to influence the evolution of the population The variation operators (crossover and mutation) use the beliefs to control the changes in individuals This is usually achieved through self-adapting control parameters, as functions of the beliefs The population space is searched using any of the standard EAs, for example an EP [720] or GA [716] Recently, particle swarm optimization (PSO) has been used on the population space level [219] The next section discusses the belief space in more detail
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Belief Space
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The belief space serves as a knowledge repository, where the collective behaviors (or beliefs) of the individuals in the population space are stored The belief space is also referred to as the meme pool, where a meme is a unit of information transmitted by behavioral means The belief space serves as a global knowledge repository of behavioral traits The memes within the belief space are generalizations of the experience of individuals within the population space These experiential generalizations are accumulated and shaped over several generations, and not just one generation These generalizations express the beliefs as to what the optimal behavior of individuals constitutes
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