Using feedback in a regulatory network computational device

Abstract

The relationship between the genotype and the phenotype in Evolutionary Algorithms (EA) is a recurrent issue among researchers. Based on our current understanding of the multitude of the regulatory mechanisms that are fundamental in both processes of inheritance and of development in natural systems, some researchers start exploring computationally this new insight, including those mechanism in the EA. The Artificial Gene Regulatory (ARN) model, proposed by Wolfgang Banzhaf was one of the first tentatives. Following his seminal work some variants were proposed with increased capabilities. In this paper, we present another modification of this model, consisting in the use the regulatory network as a computational device where feedback edges are used. Using two classical benchmarks, the n-bit parity and the Fibonacci sequence problems, we show experimentally the effectiveness of the proposal.

The Regulatory Network Computational Device

“…The presented model has been applied successfully to a diverse set of benchmark problems [35][36][37]. Although successful, especially when using the new variation operators, limitations have been found on scalability.…”

“…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.…”

“…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). In this work we explore the capabilities of this method by experimentally testing it on recursive and nonrecursive domains with standard GP benchmarks, also devising a problem similar to the factorial in order to study the scalability of the approach.…”

Developments on the Regulatory Network Computational Device

“…For instance, problems where memory is necessary or with inherent recursion may be approached differently. For that purpose the possibility of using feedback connections in the program's graph was added to the original model (Lopes & Costa, 2011b, 2011c.…”