Symbiosis, complexification and simplicity under GP

Lichodzijewski, Peter; Heywood, Malcolm I.

doi:10.1145/1830483.1830640

Cited by 41 publications

(25 citation statements)

References 18 publications

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“…The results of the control algorithms, some presented in the format of box plots/violin plots, were gathered from [9,10,15,18]. Violin plots are a combination of a box plot and a kernel density plot which shows the probability density of the data at different values.…”

Section: Discussionmentioning

confidence: 99%

“…This property is ideal to demonstrate how the idea of group selection helps to maintains individuals for both minor and major classes. Bupa and Pima are two datasets known for poor class separability; the rate of error has been observed in the region of 30% across a wide range of machine learning algorithms [10]. Other reasons to select those datasets are that they have established performance levels [9,10,15,18], and they all represent real-world data, rather than artificial data.…”

Section: Methodsmentioning

confidence: 99%

“…Bupa and Pima are two datasets known for poor class separability; the rate of error has been observed in the region of 30% across a wide range of machine learning algorithms [10]. Other reasons to select those datasets are that they have established performance levels [9,10,15,18], and they all represent real-world data, rather than artificial data. Therefore, the results obtained on those datasets make us sufficiently confident to judge if the new computational multilevel selection framework is applicable to solve real-world problems.…”

Section: Methodsmentioning

confidence: 99%

“…Well-known team-based methods include GP Teaming [3], Orthogonal Evolution of Teams (OET) [14], Cooperative Coevolutionary Evolutionary Algorithms (CCEA) [12] 1 , Individual Evolution (IE) [4,11] and Symbiotic Bid-Based GP (SBB) [10].…”

Section: Related Workmentioning

confidence: 99%

“…With respect to cooperative GP, considerable research has been devoted to investigate team evolution, major research work including GP Teaming [3], Orthogonal Evolution of Teams (OET) [14], and Symbiotic Bid-Based GP (SBB) [10]. A team, referring to a solution, is an aggregation of some individuals (subcomponents) that are evolved simultaneously in one population.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Rethinking multilevel selection in genetic programming

Banzhaf

2011

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

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This paper aims to improve the capability of genetic programming to tackle the evolution of cooperation: evolving multiple partial solutions that collaboratively solve structurally and functionally complex problems. A multilevel genetic programming approach is presented based on a new computational multilevel selection framework [19]. This approach considers biological group selection theory to encourage cooperation, and a new cooperation operator to build solutions hierarchically. It extends evolution from individuals to multiple group levels, leading to good performance on both individuals and groups. The applicability of this approach is evaluated on 7 multi-class classification problems with different features, such as non-linearity, skewed data distribution and large feature space. The results, when compared to other cooperative evolutionary algorithms in the literature, demonstrate that this approach improves solution accuracy and consistency, and simplifies solution complexity. In addition, the problem is decomposed as a result of evolution without human interference.

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Section: Discussionmentioning

confidence: 99%