A Study On Genetic Programming

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Genetic Programming GP is an evolutionary computing method that uses the principle of Darwinian natural selection in order to create computer programs to solve a problem. Koza introduced GP (1992) as a branch of genetic algorithm. GP covers a high level of diversity by breeding random different computer program as the population. The programs are structured like trees containing functions and terminals. A function set and a terminal set should be defined as the sources of functions and terminals. If they are rich enough, tree structures are able to reach any complexity. The function set, for example, can be built of arithmetic operation (+, −, ×, or ÷), Boolean logic functions (AND, OR, NOT, etc), mathematical functions (sin, cos, log), conditional functions (IF, THEN, ELSE), or any other functions. The terminal set can be included of variables, numerical constants, functions with no argument, etc. Random functions and terminals form a treelike structure program. Branches that contain functions and terminals are connected to the root point. A typical example of GP tree is shown in (FIG) Defining terminal and function sets actually determine the search space. GP starts a blind search for solution by creating random initial population within the search area. GP measure the fitness of each individual and the ones with better fitness are chosen for building the next generation by mutation, cross over or direct reproduction [1]. During the reproduction, some of the population
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