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264
Foundations of Genetic Programming
, 2002
"... The goal of getting computers to automatically solve problems is central to artificial intelligence, machine learning, and the broad area encompassed by what Turing called “machine intelligence ” [161, 162]. ..."
Abstract

Cited by 219 (65 self)
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The goal of getting computers to automatically solve problems is central to artificial intelligence, machine learning, and the broad area encompassed by what Turing called “machine intelligence ” [161, 162].
A Comparison of Linear Genetic Programming and Neural Networks in Medical Data Mining
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 2000
"... We apply linear genetic programming to several diagnosis problems in medicine. An efficient algorithm is presented that eliminates intron code in linear genetic programs. This results in a significant speedup which is especially interesting when operating with complex datasets as they are occuring ..."
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Cited by 92 (12 self)
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We apply linear genetic programming to several diagnosis problems in medicine. An efficient algorithm is presented that eliminates intron code in linear genetic programs. This results in a significant speedup which is especially interesting when operating with complex datasets as they are occuring in realworld applications like medicine. We compare our results to those obtained with neural networks and argue that genetic programming is able to show similar performance in classification and generalization even within a relatively small number of generations.
Understanding the crucial role of attribute interaction in data mining
 Artif. Intel. Rev
, 2001
"... This is a review paper, whose goal is to significantly improve our understanding of the crucial role of attribute interaction in data mining. The main contributions of this paper are as follows. Firstly, we show that the concept of attribute interaction has a crucial role across different kinds of p ..."
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Cited by 48 (14 self)
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This is a review paper, whose goal is to significantly improve our understanding of the crucial role of attribute interaction in data mining. The main contributions of this paper are as follows. Firstly, we show that the concept of attribute interaction has a crucial role across different kinds of problem in data mining, such as attribute construction, coping with small disjuncts, induction of firstorder logic rules, detection of Simpson’s paradox, and finding several types of interesting rules. Hence, a better understanding of attribute interaction can lead to a better understanding of the relationship between these kinds of problems, which are usually studied separately from each other. Secondly, we draw attention to the fact that most rule induction algorithms are based on a greedy search which does not cope well with the problem of attribute interaction, and point out some alternative kinds of rule discovery methods which tend to cope better with this problem. Thirdly, we discussed several algorithms and methods for discovering interesting knowledge that, implicitly or explicitly, are based on the concept of attribute interaction.
Evolving Objects: a general purpose evolutionary computation library
, 2001
"... This paper presents the evolving objects library (EOlib), an objectoriented framework for evolutionary computation (EC) that aims to provide a exible set of classes to build EC applications. EOlib design objective is to be able to evolve any object in which tness makes sense. ..."
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Cited by 36 (5 self)
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This paper presents the evolving objects library (EOlib), an objectoriented framework for evolutionary computation (EC) that aims to provide a exible set of classes to build EC applications. EOlib design objective is to be able to evolve any object in which tness makes sense.
Effective Linear Genetic Programming
 Neural Networks in Medical Data Mining” IEEE Transactions on Evolutionary Computation
, 2001
"... Different variants of genetic operators are introduced and compared for linear genetic programming including program induction without crossover. Variation strength of crossover and mutations is controlled based on the genetic code. Effectivity of genetic operations improves on code level and on fit ..."
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Cited by 34 (2 self)
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Different variants of genetic operators are introduced and compared for linear genetic programming including program induction without crossover. Variation strength of crossover and mutations is controlled based on the genetic code. Effectivity of genetic operations improves on code level and on fitness level. Thereby algorithms for creating code efficient solutions are presented.
What makes a problem GPhard? analysis of a tunably difficult problem in genetic programming. Genetic Programming and Evolvable Machines
, 2001
"... This paper addresses the issue of what makes a problem GPhard by considering the binomial3 problem. In the process, we discuss the efficacy of the metaphor of an adaptive fitness landscape to explain what is GPhard. We show that for at least this problem, the metaphor is misleading. 1 ..."
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Cited by 34 (6 self)
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This paper addresses the issue of what makes a problem GPhard by considering the binomial3 problem. In the process, we discuss the efficacy of the metaphor of an adaptive fitness landscape to explain what is GPhard. We show that for at least this problem, the metaphor is misleading. 1
Combinatorial Landscapes
 SIAM REVIEW
, 2002
"... Fitness landscapes have proven to be a valuable concept in evolutionary biology, combinatorial optimization, and the physics of disordered systems. A fitness landscape is a mapping from a configuration space into the real numbers. The configuration space is equipped with some notion of adjacency, ne ..."
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Cited by 33 (2 self)
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Fitness landscapes have proven to be a valuable concept in evolutionary biology, combinatorial optimization, and the physics of disordered systems. A fitness landscape is a mapping from a configuration space into the real numbers. The configuration space is equipped with some notion of adjacency, nearness, distance or accessibility. Landscape theory has emerged as an attempt to devise suitable mathematical structures for describing the "static" properties of landscapes as well as their influence on the dynamics of adaptation. In this review we focus on the connections of landscape theory with algebraic combinatorics and random graph theory, where exact results are available.
Genetic Algorithms and Support Vector Machines for Time Series Classification
, 2002
"... We introduce an algorithm for classifying time series data. Since our initial application is for lightning data, we call the algorithm Zeus. Zeus is a hybrid algorithm that employs evolutionary computation for feature extraction, and a support vector machine for the final "backend" classification. S ..."
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Cited by 32 (2 self)
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We introduce an algorithm for classifying time series data. Since our initial application is for lightning data, we call the algorithm Zeus. Zeus is a hybrid algorithm that employs evolutionary computation for feature extraction, and a support vector machine for the final "backend" classification. Support vector machines have a reputation for classifying in highdimensional spaces without overfitting, so the utility of reducing dimensionality with an intermediate feature selection step has been questioned. We address this question by testing Zeus on a lightning classification task using data acquired from the Fast Onorbit Recording of Transient Events (FORTE) satellite.
Exact Schema Theory for Genetic Programming and Variablelength Genetic Algorithms with OnePoint Crossover
, 2001
"... A few schema theorems for Genetic Programming (GP) have been proposed in the literature in the last few years. Since they consider schema survival and disruption only, they can only provide a lower bound for the expected value of the number of instances of a given schema at the next generation rathe ..."
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Cited by 30 (16 self)
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A few schema theorems for Genetic Programming (GP) have been proposed in the literature in the last few years. Since they consider schema survival and disruption only, they can only provide a lower bound for the expected value of the number of instances of a given schema at the next generation rather than an exact value. This paper presents theoretical results for GP with onepoint crossover which overcome this problem. Firstly, we give an exact formulation for the expected number of instances of a schema at the next generation in terms of microscopic quantities. Thanks to this formulation we are then able to provide an improved version of an earlier GP schema theorem in which some (but not all) schema creation events are accounted for. Then, we extend this result to obtain an exact formulation in terms of macroscopic quantities which makes all the mechanisms of schema creation explicit. This theorem allows the exact formulation of the notion of effective fitness in GP and opens the way to future work on GP convergence, population sizing, operator biases, and bloat, to mention only some of the possibilities.
Evolving Teams of Predictors with Linear Genetic Programming
 Genetic Programming and Evolvable Machines
, 2001
"... This paper applies the evolution of GP teams to di#erent classification and regression problems and compares di#erent methods for combining the outputs of the team programs. These include hybrid approaches where (1) a neural network is used to optimize the weights of programs in a team for a comm ..."
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Cited by 29 (3 self)
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This paper applies the evolution of GP teams to di#erent classification and regression problems and compares di#erent methods for combining the outputs of the team programs. These include hybrid approaches where (1) a neural network is used to optimize the weights of programs in a team for a common decision and (2) a realnumbered vector (the representation of evolution strategies) of weights is evolved with each team in parallel. The cooperative team approach results in an improved training and generalization performance compared to the standard GP method. The higher computational overhead of team evolution is counteracted by using a fast variant of linear GP.