ReNCoDe: A Regulatory Network Computational Device

Lopes, Rui L.; Costa, Ernesto

doi:10.1007/978-3-642-20407-4_13

Cited by 10 publications

(10 citation statements)

References 6 publications

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“…If some determined problem has more than one input, for instance four, then the majority rule is applied over the terminal protein signature (its binary stream) in order to define which input it corresponds to. replace bindings by edge (e-h) Finally, in order to improve the evolvability of the genomes, in [35] the authors proposed also variation operators inspired by the concepts of transposons and noncoding DNA.…”

Section: Extracting Circuits From Arnsmentioning

confidence: 99%

“…7 The modified ARN nor introduces external genetic products. A different approach, presented in [35,36,37], that uses the ARN architecture as the genotypical representation for a new computational model will be described in detail in the following sections.…”

Section: Computational Devicementioning

confidence: 99%

“…Earlier, Wolfgang Banzhaf [1] had proposed an artificial gene regulatory network(ARN) model and showed how it could be used computationally in different settings [7,41]. More recently [35] presented a variant of the ARN aimed at solving some weaknesses of that model and enlarging its applicability, by transforming the regulatory gene network into a computable graph, similarly to what is done in GP. In [37] we complement and enhance this latter proposal by making possible the use of feedback edges (equivalent to delays in an iterative process).…”

Section: Introductionmentioning

confidence: 97%

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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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Section: Extracting Circuits From Arnsmentioning

confidence: 99%

Section: Computational Devicementioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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The Regulatory Network Computational Device

Lopes

Costa

2012

Genet Program Evolvable Mach

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“…Genetic Regulatory Networks (GRNs) are a key element of gene expression regulation in biological organisms, and one that has seen recent attention in the EC field [1,10,13,11,5]. GRN-based algorithms explore the idea of differental gene expression through regulatory processes, and as such are potentially useful for dynamic and noisy environments.…”

Section: Introductionmentioning

confidence: 99%

“…It was shown to exhibit similar dynamics to its natural counterparts, such as the appearance of specific regulatory network motifs [3] and the resulting network topologies [8], and has been evolved to optimise those topologies [12]; the resulting networks have also been extracted and used as a computational device, for a subset of Genetic Programming benchmark problems [11]. The resulting complex regulatory dynamics have also been studied, from the evolution of oscillatory dynamics [10] to actual control problems such as the pole balancing benchmark [13], and also the flag-colouring developmental problem [5].…”

Section: Introductionmentioning

confidence: 99%

Applying Genetic Regulatory Networks to Index Trading

Nicolau

O’Neill

Brabazon

2012

Lecture Notes in Computer Science

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Abstract. This paper explores the computational power of genetic regulatory network models, and the practicalities of applying these to realworld problems. The specific domain of financial trading is tackled; this is a problem where time-dependent decisions are critical, and as such benefits from the differential gene expression that these networks provide. The results obtained are on par with the best found in the literature, and highlight the applicability of these models to this type of problem.

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