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The belief space contains a number of knowledge components to represent the behavioral patterns of individuals from the population space The types of knowledge components and data structures used to represent the knowledge depends on the problem being solved The rst application of CAs used version spaces represented as a lattice to store schemata [716] For function optimization, vector representations are used (discussed below) [720] Other representations that have been used include fuzzy systems [719, 725], ensemble structures [722], and hierarchical belief spaces [420] In general, the belief space contains at least two knowledge components [720]: A situational knowledge component, which keeps track of the best solutions found at each generation A normative knowledge component, which provides standards for individual behaviors, used as guidelines for mutational adjustments to individuals In the case of function optimization, the normative knowledge component maintains a set of intervals, one for each dimension of the problem being solved These intervals characterize the range of what is believed to be good areas to search in each dimension If only these two components are used, the belief space is represented as the tuple, B(t) = (S(t), N (t)) (141) where S(t) represents the situational knowledge component, and N (t) represents the normative knowledge component The situational component is the set of best solutions, (142) S(t) = { l (t) : l = 1, , nS } y

143 Belief Space and the normative component is represented as N (t) = (X1 (t), X2 (t), , Xnx (t)) where, for each dimension, the following information is stored: Xj (t) = (Ij (t), Lj (t), Uj (t))

5

(143)

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(144)

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Ij denotes the closed interval, Ij (t) = [xmin,j (t), xmax,j (t)] = {x : xmin,j x xmax,j }, Lj (t) is the score for the lower bound, and Uj (t) is the score for the upper bound In addition to the above knowledge components, the following knowledge components can be added [672, 752]: A domain knowledge component, which is similar to the situational knowledge component in that it stores the best positions found The domain knowledge component di ers from the situational knowledge component in that knowledge is not re-initialized at each generation, but contains an archive of best solutions since evolution started very similar to the hall-of-fame used in coevolution A history knowledge component, used in problems where search landscapes may change This component maintains information about sequences of environmental changes For each environmental change, the following information is stored: the current best solution, the directional change for each dimension and the current change distance A topographical knowledge component, which maintains a multi-dimensional grid representation of the search space Information is kept about each cell of the grid, eg frequency of individuals that occupy the cell Such frequency information can be used to improve exploration by forcing mutation direction towards unexplored areas The type of knowledge components and the way that knowledge is represented have an in uence on the acceptance and in uence functions, as discussed in the following sections

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