The Experts below are selected from a list of 52809 Experts worldwide ranked by ideXlab platform
Daryl Essam - One of the best experts on this subject based on the ideXlab platform.
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representation and structural difficulty in Genetic Programming
IEEE Transactions on Evolutionary Computation, 2006Co-Authors: Nguyen Xuan Hoai, R I Mckay, Daryl EssamAbstract:Standard tree-based Genetic Programming suffers from a structural difficulty problem in that it is unable to search effectively for solutions requiring very full or very narrow trees. This deficiency has been variously explained as a consequence of restrictions imposed by the tree structure or as a result of the numerical distribution of tree shapes. We show that by using a different tree-based representation and local (insertion and deletion) structural modification operators, that this problem can be almost eliminated even with trivial (stochastic hill-climbing) search methods, thus eliminating the above explanations. We argue, instead, that structural difficulty is a consequence of the large step size of the operators in standard Genetic Programming, which is itself a consequence of the fixed-arity property embodied in its representation.
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solving the symbolic regression problem with tree adjunct grammar guided Genetic Programming the comparative results
Congress on Evolutionary Computation, 2002Co-Authors: Nguyen Xuan Hoai, R I Mckay, Daryl Essam, R ChauAbstract:In this paper, we show some experimental results of tree-adjunct grammar-guided Genetic Programming (TAG3P) on the symbolic regression problem, a benchmark problem in Genetic Programming. We compare the results with Genetic Programming (GP) and grammar-guided Genetic Programming (GGGP). The results show that TAG3P significantly outperforms GP and GGGP on the target functions attempted in terms of the probability of success. Moreover, TAG3P still performed well when the structural complexity of the target function was scaled up.
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Some experimental results with tree adjunct grammar guided Genetic Programming
Springer-Verlag, 2002Co-Authors: N. X. Hoai, R I Mckay, Daryl EssamAbstract:Abstract. Tree-adjunct grammar guided Genetic Programming (TAG3P) [5] is a grammar guided Genetic Programming system that uses context-free grammars along with tree-adjunct grammars as means to set language bias for the Genetic Programming system. In this paper, we show the experimental results of TAG3P on two problems: symbolic regression and trigonometric identity discovery. The results show that TAG3P works well on those problems.
Kwongsak Leung - One of the best experts on this subject based on the ideXlab platform.
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using instruction matrix based Genetic Programming to evolve programs
International Symposium on Intelligence Computation and Applications, 2007Co-Authors: Kinhong Lee, Kwongsak LeungAbstract:In Genetic Programming (GP), evolving tree nodes separately would be an ideal approach to reduce the huge solution space of GP. We use Instruction Matrix based Genetic Programming (IMGP) to evolve tree nodes separately while taking into account their interdependencies in the form of subtrees. IMGP uses an Instruction Matrix (IM) to maintain the statistical data of tree nodes and subtrees. IMGP extracts program trees from IM, and updates IM with the information of the extracted program trees. The experiments have verified that the results of IMGP are better than those the related GP algorithms in terms of the qualities of the solutions and the number of program evaluations.
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evolve schema directly using instruction matrix based Genetic Programming
European Conference on Genetic Programming, 2005Co-Authors: Kinhong Lee, Kwongsak LeungAbstract:This paper proposes a new architecture for tree-based Genetic Programming to evolve schema directly. It uses fixed length hs-expressions to represent program trees, keeps schema information in an instruction matrix, and extracts individuals from it. In order to manipulate the instruction matrix and the hs-expression, new Genetic operators and new matrix functions are developed. The experimental results verify that its results are better than those of the canonical Genetic Programming on the problems tested in this paper.
Nguyen Xuan Hoai - One of the best experts on this subject based on the ideXlab platform.
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combining conformal prediction and Genetic Programming for symbolic interval regression
Genetic and Evolutionary Computation Conference, 2017Co-Authors: Pham Thi Thuong, Nguyen Xuan HoaiAbstract:Symbolic regression has been one of the main learning domains for Genetic Programming. However, most work so far on using Genetic Programming for symbolic regression only focus on point prediction. The problem of symbolic interval regression is for each input to find a prediction interval containing the output with a given statistical confidence. This problem is important for many risk-sensitive domains (such as in medical and financial applications). In this paper, we propose the combination of conformal prediction and Genetic Programming for solving the problem of symbolic interval regression. We study two approaches called black-box conformal prediction Genetic Programming (black-box CPGP) and white-box conformal prediction Genetic Programming (white-box CPGP) on a number of benchmarks and previously used problems. We compare the performance of these approaches with two popular interval regressors in statistic and machine learning domains, namely, the linear quantile regression and quantile random forrest. The experimental results show that, on the two performance metrics, black-box CPGP is comparable to the linear quantile regression and not much worse than the quantile random forrest on validity and much better than them on efficiency.
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operator self adaptation in Genetic Programming
European Conference on Genetic Programming, 2011Co-Authors: Minhyeok Kim, Nguyen Xuan Hoai, R I Mckay, Kangil KimAbstract:We investigate the application of adaptive operator selection rates to Genetic Programming. Results confirm those from other areas of evolutionary algorithms: adaptive rate selection out-performs non-adaptive methods, and among adaptive methods, adaptive pursuit out-performs probability matching. Adaptive pursuit combined with a reward policy that rewards the overall fitness change in the elite worked best of the strategies tested, though not uniformly on all problems.
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representation and structural difficulty in Genetic Programming
IEEE Transactions on Evolutionary Computation, 2006Co-Authors: Nguyen Xuan Hoai, R I Mckay, Daryl EssamAbstract:Standard tree-based Genetic Programming suffers from a structural difficulty problem in that it is unable to search effectively for solutions requiring very full or very narrow trees. This deficiency has been variously explained as a consequence of restrictions imposed by the tree structure or as a result of the numerical distribution of tree shapes. We show that by using a different tree-based representation and local (insertion and deletion) structural modification operators, that this problem can be almost eliminated even with trivial (stochastic hill-climbing) search methods, thus eliminating the above explanations. We argue, instead, that structural difficulty is a consequence of the large step size of the operators in standard Genetic Programming, which is itself a consequence of the fixed-arity property embodied in its representation.
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solving the symbolic regression problem with tree adjunct grammar guided Genetic Programming the comparative results
Congress on Evolutionary Computation, 2002Co-Authors: Nguyen Xuan Hoai, R I Mckay, Daryl Essam, R ChauAbstract:In this paper, we show some experimental results of tree-adjunct grammar-guided Genetic Programming (TAG3P) on the symbolic regression problem, a benchmark problem in Genetic Programming. We compare the results with Genetic Programming (GP) and grammar-guided Genetic Programming (GGGP). The results show that TAG3P significantly outperforms GP and GGGP on the target functions attempted in terms of the probability of success. Moreover, TAG3P still performed well when the structural complexity of the target function was scaled up.
R I Mckay - One of the best experts on this subject based on the ideXlab platform.
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operator self adaptation in Genetic Programming
European Conference on Genetic Programming, 2011Co-Authors: Minhyeok Kim, Nguyen Xuan Hoai, R I Mckay, Kangil KimAbstract:We investigate the application of adaptive operator selection rates to Genetic Programming. Results confirm those from other areas of evolutionary algorithms: adaptive rate selection out-performs non-adaptive methods, and among adaptive methods, adaptive pursuit out-performs probability matching. Adaptive pursuit combined with a reward policy that rewards the overall fitness change in the elite worked best of the strategies tested, though not uniformly on all problems.
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representation and structural difficulty in Genetic Programming
IEEE Transactions on Evolutionary Computation, 2006Co-Authors: Nguyen Xuan Hoai, R I Mckay, Daryl EssamAbstract:Standard tree-based Genetic Programming suffers from a structural difficulty problem in that it is unable to search effectively for solutions requiring very full or very narrow trees. This deficiency has been variously explained as a consequence of restrictions imposed by the tree structure or as a result of the numerical distribution of tree shapes. We show that by using a different tree-based representation and local (insertion and deletion) structural modification operators, that this problem can be almost eliminated even with trivial (stochastic hill-climbing) search methods, thus eliminating the above explanations. We argue, instead, that structural difficulty is a consequence of the large step size of the operators in standard Genetic Programming, which is itself a consequence of the fixed-arity property embodied in its representation.
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solving the symbolic regression problem with tree adjunct grammar guided Genetic Programming the comparative results
Congress on Evolutionary Computation, 2002Co-Authors: Nguyen Xuan Hoai, R I Mckay, Daryl Essam, R ChauAbstract:In this paper, we show some experimental results of tree-adjunct grammar-guided Genetic Programming (TAG3P) on the symbolic regression problem, a benchmark problem in Genetic Programming. We compare the results with Genetic Programming (GP) and grammar-guided Genetic Programming (GGGP). The results show that TAG3P significantly outperforms GP and GGGP on the target functions attempted in terms of the probability of success. Moreover, TAG3P still performed well when the structural complexity of the target function was scaled up.
-
Some experimental results with tree adjunct grammar guided Genetic Programming
Springer-Verlag, 2002Co-Authors: N. X. Hoai, R I Mckay, Daryl EssamAbstract:Abstract. Tree-adjunct grammar guided Genetic Programming (TAG3P) [5] is a grammar guided Genetic Programming system that uses context-free grammars along with tree-adjunct grammars as means to set language bias for the Genetic Programming system. In this paper, we show the experimental results of TAG3P on two problems: symbolic regression and trigonometric identity discovery. The results show that TAG3P works well on those problems.
Kinhong Lee - One of the best experts on this subject based on the ideXlab platform.
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using instruction matrix based Genetic Programming to evolve programs
International Symposium on Intelligence Computation and Applications, 2007Co-Authors: Kinhong Lee, Kwongsak LeungAbstract:In Genetic Programming (GP), evolving tree nodes separately would be an ideal approach to reduce the huge solution space of GP. We use Instruction Matrix based Genetic Programming (IMGP) to evolve tree nodes separately while taking into account their interdependencies in the form of subtrees. IMGP uses an Instruction Matrix (IM) to maintain the statistical data of tree nodes and subtrees. IMGP extracts program trees from IM, and updates IM with the information of the extracted program trees. The experiments have verified that the results of IMGP are better than those the related GP algorithms in terms of the qualities of the solutions and the number of program evaluations.
-
evolve schema directly using instruction matrix based Genetic Programming
European Conference on Genetic Programming, 2005Co-Authors: Kinhong Lee, Kwongsak LeungAbstract:This paper proposes a new architecture for tree-based Genetic Programming to evolve schema directly. It uses fixed length hs-expressions to represent program trees, keeps schema information in an instruction matrix, and extracts individuals from it. In order to manipulate the instruction matrix and the hs-expression, new Genetic operators and new matrix functions are developed. The experimental results verify that its results are better than those of the canonical Genetic Programming on the problems tested in this paper.
