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74,907
Penalty Functions for Genetic Programming Algorithms
"... Abstract. Very often symbolic regression, as addressed in Genetic Programming (GP), is equivalent to approximate interpolation. This means that, in general, GP algorithms try to fit the sample as better as possible but no notion of generalization error is considered. As a consequence, overfitting, c ..."
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Abstract. Very often symbolic regression, as addressed in Genetic Programming (GP), is equivalent to approximate interpolation. This means that, in general, GP algorithms try to fit the sample as better as possible but no notion of generalization error is considered. As a consequence, overfitting
Analysis of a Genetic Programming Algorithm for Association Studies∗
"... In this paper a Genetic Programming algorithm for genetic association studies is reconsidered. It is shown, that the application field of the algorithm is not restricted to ge-netic association studies, but that the algorithm can also be applied to logic minimization problems. In the context of mult ..."
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In this paper a Genetic Programming algorithm for genetic association studies is reconsidered. It is shown, that the application field of the algorithm is not restricted to ge-netic association studies, but that the algorithm can also be applied to logic minimization problems. In the context
A Genetic Programming Algorithm for Association Studies
"... Abstract. The analysis of genetic association is useful for identifying genetic fac-tors that may contribute to a medical condition. An important subarea are case-control studies on single nucleotide polymorphism (SNP) data, i.e. data on genetic variations that occur when different base alternatives ..."
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alternatives exist at a single base pair position. The major goal of these studies is to identify SNPs and SNP interactions that lead to a higher disease risk. We present a Genetic Programming algorithm called GPAS (Genetic Program-ming for Association Studies) for case-control association studies which
Genetic Programming
, 1997
"... Introduction Genetic programming is a domain-independent problem-solving approach in which computer programs are evolved to solve, or approximately solve, problems. Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring ..."
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Cited by 1056 (12 self)
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Introduction Genetic programming is a domain-independent problem-solving approach in which computer programs are evolved to solve, or approximately solve, problems. Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring
A Classification Module for Genetic Programming Algorithms in JCLEC
"... JCLEC-Classification is a usable and extensible open source library for genetic program-ming classification algorithms. It houses implementations of rule-based methods for clas-sification based on genetic programming, supporting multiple model representations and providing to users the tools to impl ..."
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Cited by 2 (1 self)
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JCLEC-Classification is a usable and extensible open source library for genetic program-ming classification algorithms. It houses implementations of rule-based methods for clas-sification based on genetic programming, supporting multiple model representations and providing to users the tools
An Enhanced Genetic Programming Algorithm for Optimal Controller Design
, 2012
"... This paper proposes a Genetic Programming based algorithm that can be used to design optimal controllers. The pro-posed algorithm will be named a Multiple Basis Function Genetic Programming (MBFGP). Herein, the main ideas concerning the initial population, the tree structure, genetic operations, and ..."
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This paper proposes a Genetic Programming based algorithm that can be used to design optimal controllers. The pro-posed algorithm will be named a Multiple Basis Function Genetic Programming (MBFGP). Herein, the main ideas concerning the initial population, the tree structure, genetic operations
Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization
, 1993
"... The paper describes a rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to a ..."
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Cited by 633 (15 self)
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The paper describes a rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified
Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
- Evolutionary Computation
, 1994
"... In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about t ..."
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Cited by 539 (5 self)
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the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. Since genetic algorithms(GAs) work with a population of points, it seems natural to use GAs in multiobjective optimization problems to capture a
A NEW POLYNOMIAL-TIME ALGORITHM FOR LINEAR PROGRAMMING
- COMBINATORICA
, 1984
"... We present a new polynomial-time algorithm for linear programming. In the worst case, the algorithm requires O(tf'SL) arithmetic operations on O(L) bit numbers, where n is the number of variables and L is the number of bits in the input. The running,time of this algorithm is better than the ell ..."
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Cited by 860 (3 self)
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We present a new polynomial-time algorithm for linear programming. In the worst case, the algorithm requires O(tf'SL) arithmetic operations on O(L) bit numbers, where n is the number of variables and L is the number of bits in the input. The running,time of this algorithm is better than
Dynamic programming algorithm optimization for spoken word recognition
- IEEE TRANSACTIONS ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING
, 1978
"... This paper reports on an optimum dynamic programming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using timewarping function. Then, two time-normalized distance definitions, ded symmetric and asymmetric forms, are der ..."
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Cited by 788 (3 self)
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This paper reports on an optimum dynamic programming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using timewarping function. Then, two time-normalized distance definitions, ded symmetric and asymmetric forms
Results 1 - 10
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74,907
