Genetic Transposition in Tree-Adjoining Grammar Guided Genetic Programming: The Duplication Operator
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Operator (biology)
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Genetic operator
Tree (set theory)
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Like other learning paradigms, the performance of the genetic algorithms (GAs) is dependent on the parameter choice, on the problem representation, and on the fitness landscape. Accordingly, a GA can show good or weak results even when applied on the same problem. Following this idea, the crossover operator plays an important role, and its study is the object of the present paper. A mathematical analysis has led us to construct a new form of crossover operator inspired from genetic programming (GP) that we have already applied in field of information retrieval. In this paper we extend the previous results and compare the new operator with several known crossover operators under various experimental conditions.
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The Crossover operator is common to most implementations of Genetic Programming (GP). Often, there is some form of restriction on the size of trees in the GP population. This paper concentrates on the interaction between the standard crossover operator and a restriction on tree depth demonstrated by the MAX problem, which involves returning the largest possible value for given function and terminal sets and maximum tree depth.
Some characteristics and inadequacies of crossover in normal use are highlighted and discussed. Subtree discovery and movement takes place mostly near the leaf nodes, with nodes near the root left untouched, where diversity drops quickly to zero in the tree population. GP is then unable to create fitter trees via the crossover operator, leaving a mutation operator as the only common, but ineffective, route to discovery of fitter trees.
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This paper investigated evolutionary programming with operator adaptation at both population level and individual level. The fitness distributions were employed to update operators at population level while the immediate reward or punishment from the feedback of mutations was applied to change operators at individual level. Experimental results had shown that long jump operators could actually have smaller average winning step sizes. Through observing the evolution of step sizes and fitness distribution values for each mutation operator, it was discovered that small- stepping operator could become the only dominant operator while other more capable operators with long jumps had only been applied at rather low probabilities.
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Symbolic Regression
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The selection operator, the corssover operator and the mutation operator of the genetic algorithm are deeply analyzed in the paper. Then, it is proved that the main reason of the premature phenomena is the selection operator. Based on the analysis, a self-adjusting gene migration genetic algorithm with the self-changeable mutation rate is designed. And the guideline of the population differentia is put forward. The probability of mutation is automatically changed by the population differentia. So the population diversity is kept and the premature phenomena are avoided. At last, the convergence of the algorithm is proved by the Markov-Chain. The experimental results indicate that it has the ability of the universal using and is suitable for the engineering calculation.
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Premature convergence
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The performance of a Multi-Objective Evolutionary Programming (MOEP) is significantly dependent on the parameter setting of the operator. These parameters tend to change the characteristic of adaptive in different stages of evolutionary process. The intention of this paper is to create adaptive controls for each parameter existing in MOEP where it is able to improve even more the performance of the evolutionary programming. Hence, in this paper, an adaptive mutation operator based multi-objective evolutionary programming is presented. A computer program was written in MATLAB. At the end, the result was compared with the Polynomial Mutation Operator.
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Adaptive mutation
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Hybridization of genetic algorithms increases the search capabilities by means of convergence rate and speed. In this paper, we suggest to use Hooke-Jeeves algorithm as a genetic operator which performs a local search using the best chromosome in a generation as the base point. As Hooke-Jeeves algorithm searches a subspace in all directions of parameters for a given starting point, it can be considered as an intelligent mutation operator, whereas, the classical mutation operator is totally blind. The operator is applied within a predefined probability. Simulation studies performed on optimizing some well-known set of test functions show that using such an intelligent mutation operator has significant effects even for small number of iterations.
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Operator (biology)
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