The Entities of Gene Expression Programming

Ferreira, Cândida

doi:10.1007/3-540-32849-1_2

Cited by 241 publications

(199 citation statements)

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“…Gene expression programming (GEP) has been proposed as an enhancement to genetic programming (Ferreira 2006). In a GEP-type approach, solutions are encoded as a linear sequence of functions and terminals during the selection, recombination, and mutation operations.…”

Section: Motivationmentioning

confidence: 99%

“…Therefore, the phenotypic search space over which the algorithm must explore is highly discontinuous as changing one node or terminal, analogous to moving to a neighboring point in the search space, can yield a major change in the behavior and therefore the score of the solution. Gene expression programming (GEP) has been proposed to help address these problems by encoding the different sizes and shapes of parse trees in linear solutions of fixed length (Ferreira 2006). These linear solutions are then mapped to parse trees that can be simulated.…”

Section: )mentioning

confidence: 99%

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Integration of Reaction Kinetics Theory and Gene Expression Programming to Infer Reaction Mechanism

White

Srivastava

2017

Applications of Evolutionary Computation

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Mechanistic mathematical models of biological systems have been used to describe biological phenomena, including human disease, in the hope that one day these models may be used to better understand diseases, as well as to develop and optimize therapeutic strategies. Evolutionary algorithms, such as genetic programming, may be used to symbolically regress mathematical models describing chemical and biochemical species for which kinetic data are available. However, current evolutionary algorithms are restricted to the formulation of simple or approximate models due to the computational cost of evolving mechanistic models for more complex systems.It was hypothesized that chemical reaction kinetic theory could be used to sufficiently reduce the model search space for an evolutionary algorithm such that it would be possible to infer mechanistic mathematical models of complex biologicalinteractions. An evolutionary algorithm capable of formulating mass action kinetic models of biological systems from time series data sets was developed for a system of nspecies using heuristics from chemical reaction kinetic theory and a gene expression programming (GEP) based approach.Jason Robert White -University of Connecticut, 2014The resulting algorithm was then successfully validated on a general model of viral dynamics that accounted for six pathways relating the change in viral template, viral genome, and viral structural protein concentrations over time.The algorithm was applied to generate cohort-specific models of HIV dynamics from a clinical data set. HIV-1 infection models were defined as sets of two ordinary differential equations describing the change in CD4 + T-cell and HIV-1 concentrations over time. The evolved models were used to generate hypotheses regarding treatment effectiveness and the potential for viral rebound in three cohorts of HIV-1 positive individuals receiving different Highly Active Antiretroviral Therapy (HAART) regimens.It was hypothesized by the algorithm that HAART was effective in stopping HIV-1 propagation in two of the three cohorts studied. In the other cohort, it was hypothesized that HIV-1 continued to propagate and that there was the potential for viral rebound.The result of this work was the development of an algorithm that can be used for the generation of complex mechanistic biological models based upon kinetic data with potential uses in fields ranging from biomedical to biotechnological.

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Section: Motivationmentioning

confidence: 99%

Section: )mentioning

confidence: 99%

Integration of Reaction Kinetics Theory and Gene Expression Programming to Infer Reaction Mechanism

White

Srivastava

2017

Applications of Evolutionary Computation

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“…During the last years we saw the appearance of novel representations and the corresponding genotype to phenotype mapping: grammatical evolution [43], selfmodifying cartesian genetic programming [39], gene expression programming [11], or enzyme programming [34]. Along a different path, in a seminal paper W. Banzhaf and colleagues [2], suggested that one should enrich the artificial model of evolution with the inclusion of feedback regulatory mechanisms.…”

Section: Introductionmentioning

confidence: 99%

The Regulatory Network Computational Device

Lopes

Costa

2012

Genet Program Evolvable Mach

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Evolutionary Algorithms (EA) approach the genotype-phenotype relationship differently than does nature, and this discrepancy is a recurrent issue among researchers. Moreover, in spite of some performance improvements, it is a fact that biological knowledge has advanced faster than our ability to incorporate novel biological ideas into EAs. Recently, some researchers have started exploring computationally new comprehension of the multitude of the regulatory mechanisms that are fundamental in both processes of inheritance and of development in natural systems, by trying to include those mechanisms in the EAs. One of the first successful proposals was the Artificial Gene Regulatory Network (ARN) model, by Wolfgang Banzhaf. Soon after some variants of the ARN were tested. In this paper, we describe one of those, the Regulatory Network Computational Device, demonstrating experimentally its capabilities. The efficacy and efficiency of this alternative is tested experimentally using typical benchmark problems for Genetic Programming (GP) systems. We devise a modified factorial problem to investigate the use of feedback connections and the scalability of the approach. In order to gain a better understanding about the reasons for the improved quality of the results, we undertake a preliminary study about the role of neutral mutations during the evolutionary process.

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“…Inspired by Darwin's theory of evolution, Ferreira introduced genetic expression programing (GEP), which brings the optimum selection of input variables in regressions/function findings [27]. The GEP is used in many engineering disciplines [28][29][30][31], and its operating functions are subjected to a vigorous learning process to find the optimum ones to use in the gene structures.…”

Section: Introductionmentioning

confidence: 99%

Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts

Shabani

Candelieri

Archetti

et al. 2018

Water

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This article proposes a new general approach in short-term water demand forecasting based on a two-stage learning process that couples time-series clustering with gene expression programming (GEP). The approach was tested on the real life water demand data of the city of Milan, in Italy. Moreover, multi-scale modeling using a series of head-time was deployed to investigate the optimum temporal resolution under study. Multi-scale modeling was performed based on rearranging hourly based patterns of water demand into 3, 6, 12, and 24 h lead times. Results showed that GEP should receive more attention among the emerging nonlinear modelling techniques if coupled with unsupervised learning algorithms in detailed spherical k-means.

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The Entities of Gene Expression Programming

Cited by 241 publications

References 0 publications

Integration of Reaction Kinetics Theory and Gene Expression Programming to Infer Reaction Mechanism

Integration of Reaction Kinetics Theory and Gene Expression Programming to Infer Reaction Mechanism

The Regulatory Network Computational Device

Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts

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