Foundations of Genetic Programming

Langdon, William B.; Poli, Roberto

doi:10.1007/978-3-662-04726-2

Cited by 631 publications

(471 citation statements)

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“…It has been applied successfully to broad fields such as automatic design, pattern recognition, data mining, robotic control, synthesis of artificial neural architectures, bioinformatics, music and picture generation (Langdon & Poli 2002). Compared to black-box data mining methods, GP provides the potential to gain insight into the relationships between the variables.…”

Section: Genetic Programming (Gp)mentioning

confidence: 99%

Application of genetic programming to modeling pipe failures in water distribution systems

Chen

2010

Journal of Hydroinformatics

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The water loss from a water distribution system is a serious problem for many cities, which incurs enormous economic and social loss. However, the economic and human resource costs to exactly locate the leakage are extraordinarily high. Thus, reliable and robust pipe failure models are demanded to assess a pipe's propensity to fail. Beijing City was selected as the case study area and the pipe failure data for 19 years (1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005) were analyzed. Three different kinds of methods were applied to build pipe failure models. First, a statistical model was built, which discovered that the ages of leakage pipes followed the Weibull distribution. Then, two other models were developed using genetic programming (GP) with different data pre-processing strategies. The three models were compared thereafter and the best model was applied to assess the criticality of all the pipe segments of the entire water supply network in Beijing City based on GIS data.

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Section: Genetic Programming (Gp)mentioning

confidence: 99%

Application of genetic programming to modeling pipe failures in water distribution systems

Chen

2010

Journal of Hydroinformatics

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“…One concept that underlies many of these approaches is the notion of fitness landscape [15]. A fitness landscape is a plot where the points in the horizontal subspace represent the different individual genotypes in a search space and the points in the vertical direction represent their fitness [9]. Individual genotypes are usually placed on the horizontal subspace according to a certain neighborhood structure.…”

Section: Introductionmentioning

confidence: 99%

Negative Slope Coefficient: A Measure to Characterize Genetic Programming Fitness Landscapes

Vanneschi

Tomassini

Collard

et al. 2006

Lecture Notes in Computer Science

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Abstract. Negative slope coefficient has been recently introduced and empirically proven a suitable hardness indicator for some well known genetic programming benchmarks, such as the even parity problem, the binomial-3 and the artificial ant on the Santa Fe trail. Nevertheless, the original definition of this measure contains several limitations. This paper points out some of those limitations, presents a new and more relevant definition of the negative slope coefficient and empirically shows the suitability of this new definition as a hardness measure for some genetic programming benchmarks, including the multiplexer, the intertwined spirals problem and the royal trees.

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“…There has also been some work in this area in GP [8], although the tree structures employed by GP complicate the matter [14], as the manner in which the meaning of nodes often depends on the way in which they are used, and the nodes above them. In particular, the root and other nodes around it dictate how the other nodes operate.…”

Section: Introductionmentioning

confidence: 99%

A Competitive Building Block Hypothesis

Ryan

Majeed

Azad

2004

Genetic and Evolutionary Computation – GECCO 2004

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Abstract. This paper is concerned with examining the way in which rooted building blocks grow in GP-like systems. We hypothesize that, in addition to the normal notion of co-operative building blocks, there are also competitive building blocks in the population. These competitive building blocks are all of the rooted variety, all share a similar root structure and compete with each other to spread their particular extensions to the common structure throughout the population. We demonstrate that not only do these competitive building blocks exist, but that they work in tandem with non-rooted co-operative building blocks.

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Foundations of Genetic Programming

Cited by 631 publications

References 0 publications

Application of genetic programming to modeling pipe failures in water distribution systems

Application of genetic programming to modeling pipe failures in water distribution systems

Negative Slope Coefficient: A Measure to Characterize Genetic Programming Fitness Landscapes

A Competitive Building Block Hypothesis

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