Multi-population Parallel Genetic Algorithm for Economic Statistical Information Mining Based on Gene Expression Programming

Liu, Weiqi; Li, Tian-de; Tang, Changjie; Liu, Qiwei; Li, Chuan; Qiao, Shaojie

doi:10.1109/icnc.2007.487

“…The DM-GEP algorithm [33] introduced a dynamic mutation rate operator in an attempt to guide evolution. DM-GEP divided the execution of a run into three stages, the initial stage, the metaphase stage and the anaphase stage.…”

Section: Parameter Tuning and Self-adaptationmentioning

confidence: 99%

Adaptive representations for improving evolvability, parameter tuning, and parallelization of gene expression programming

Browne¹

2021

Preprint

0

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Gene Expression Programming (GEP) is a genetic algorithm that evolves linear chromosomes encoding nonlinear (tree-like) structures. In the original GEP algorithm, the genome size is problem specific and is determined through trial and error. In this work, a novel method for adaptively tuning the genome size is presented. The approach introduces new mutation, transposition and recolI)bination operators that enable a population of heterogeneously structured chromosomes, something the original GEP algorithm does not support. This permits crossbreeding between normally incompatible individuals, speciation within a population, increases the evolvability of the representations and enhances parallel GEP. To test our approach an assortment of problems were used, including symbolic regression, classification and parameter optimization. Our experimental results show that our approach provides a solution for the problem of self-adaptively tuning the genome size of GEP's representation.

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“…The DM-GEP algorithm [33] introduced a dynamic mutation rate operator in an attempt to guide evolution. DM-GEP divided the execution of a run into three stages, the initial stage, the metaphase stage and the anaphase stage.…”

Section: Parameter Tuning and Self-adaptationmentioning

confidence: 99%

Adaptive representations for improving evolvability, parameter tuning, and parallelization of gene expression programming

Browne¹

2021

Preprint

0

View full text Add to dashboard Cite

Gene Expression Programming (GEP) is a genetic algorithm that evolves linear chromosomes encoding nonlinear (tree-like) structures. In the original GEP algorithm, the genome size is problem specific and is determined through trial and error. In this work, a novel method for adaptively tuning the genome size is presented. The approach introduces new mutation, transposition and recolI)bination operators that enable a population of heterogeneously structured chromosomes, something the original GEP algorithm does not support. This permits crossbreeding between normally incompatible individuals, speciation within a population, increases the evolvability of the representations and enhances parallel GEP. To test our approach an assortment of problems were used, including symbolic regression, classification and parameter optimization. Our experimental results show that our approach provides a solution for the problem of self-adaptively tuning the genome size of GEP's representation.

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“…The PED-GEP algorithm introduced in [33] used a measure of diversity to guide evolution among parallel clients; however, the details of their parallelization lacked further specifics.…”

Section: Distributed Evolutionmentioning

confidence: 99%

“…The DM-GEP algorithm [33] introduced a dynamic mutation rate operator in an attempt guide evolution. DM-GEP divided the execution of a run into three stages, the initial stage; the metaphase stage, and the anaphase stage.…”

Section: Applied Computational Intelligence and Soft Computingmentioning

confidence: 99%

Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming

Browne

¹

,

Santos

²

2010

Applied Computational Intelligence and Soft Computing

View full text Add to dashboard Cite

Gene Expression Programming (GEP) is a genetic algorithm that evolves linear chromosomes encoding nonlinear (tree-like) structures. In the original GEP algorithm, the genome size is problem specific and is determined through trial and error. In this work, a method for adaptive control of the genome size is presented. The approach introduces mutation, transposition, and recombination operators that enable a population of heterogeneously structured chromosomes, something the original GEP algorithm does not support. This permits crossbreeding between normally incompatible individuals, speciation within a population, increases the evolvability of the representations, and enhances parallel GEP. To test our approach, an assortment of problems were used, including symbolic regression, classification, and parameter optimization. Our experimental results show that our approach provides a solution for the problem of self-adaptive control of the genome size of GEP's representation.

show abstract

Multi-population Parallel Genetic Algorithm for Economic Statistical Information Mining Based on Gene Expression Programming

Cited by 4 publications

References 2 publications

Adaptive representations for improving evolvability, parameter tuning, and parallelization of gene expression programming

Adaptive representations for improving evolvability, parameter tuning, and parallelization of gene expression programming

Adaptive representations for improving evolvability, parameter tuning, and parallelization of gene expression programming

Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming

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