genetic programming (GP) is evolutionary computation (Evolutionary Computation, EC) as a model. EC is an evolutionary mechanism to learn from nature, arising from the parallel random search algorithm. The basic principle of evolutionary algorithms to select and change, it is different from other search method has two significant characteristics: First, these algorithms are based on population (population) of; followed by individuals in the population (indvidual) between the competition. To search for specific (interest) query need a tool that intelligently generates a set of queries and whether they can export the user given the same set of objects to be evaluated. GP algorithm of this type of problem is very practical.
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1 2 function set and end set to create the initial population value of select properties 3 4 5 Evaluation of breeding new populations (the fitness function measurement) a set of common function set and the endpoint can be generated GP The assembly is a user-defined set of functions and endpoint set. Table 1 shows the corresponding function set and end set, which set of functions defined by the 1.3 query operator, logical operators and comparison operators formed. Function set {SEL, REL, G-REL,
paul smith wallets, RES}, {UNI, INT, DIF}, {AND, OR, NOT}, {>,>=,=, 0, T ≥ hi and i = 1, 2, ..., population size (T is a set of objects identified potential, hi is an individual number of the selected query i, ni is a potential query result set i). The fitness function depends on the hi and ni, if a query is not already selected (hi = 0), the function of the value of T, which is one of the worst fitness value. On the other hand, if the query results can match very well presented set of objects to the system, then its fitness value is 0 (in this case hi = ni = T). If the populations of individual fitness appears far more than the average population fitness, the individuals in the population will soon be an absolute proportion to the phenomenon of premature convergence. On the other hand, in the latter part of the search process, the group's average fitness value may be close to optimal fitness groups, resulting in improved search for the target is difficult, there stagnation [4]. To prevent this from happening, we will query an individual example of the number of ni as the denominator. More Atlas Atlas term
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