Neutral Networks and Evolvability with Complex Genotype-Phenotype Mapping

Smith, Tom; Husbands, Phil; O’Shea, Michael

doi:10.1007/3-540-44811-x_29

Cited by 34 publications

(25 citation statements)

References 16 publications

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“…Conflicting opinions have been proposed with regard to the effects of neutrality on evolutionary search. While some studies have found no benefit [2,3,4], others have claimed that neutrality provides a buffer against deleterious genetic perturbation [5,6] and reduces the risk of premature convergence through an expansion of the search space [7,8]. As argued in [9], the lack of consensus regarding the benefits of neutrality largely stem from the overly complex problems, representations, and search algorithms used in these analyses, which make it difficult to tease apart the effects of neutrality from other confounding factors.…”

Section: Introductionmentioning

confidence: 99%

Robustness, Evolvability, and Accessibility in Linear Genetic Programming

Payne

Banzhaf

et al. 2011

Lecture Notes in Computer Science

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Abstract. Whether neutrality has positive or negative effects on evolutionary search is a contentious topic, with reported experimental results supporting both sides of the debate. Most existing studies use performance statistics, e.g., success rate or search efficiency, to investigate if neutrality, either embedded or artificially added, can benefit an evolutionary algorithm. Here, we argue that understanding the influence of neutrality on evolutionary optimization requires an understanding of the interplay between robustness and evolvability at the genotypic and phenotypic scales. As a concrete example, we consider a simple linear genetic programming system that is amenable to exhaustive enumeration, and allows for the full characterization of these properties. We adopt statistical measurements from RNA systems to quantify robustness and evolvability at both genotypic and phenotypic levels. Using an ensemble of random walks, we demonstrate that the benefit of neutrality crucially depends upon its phenotypic distribution.

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Section: Introductionmentioning

confidence: 99%

Robustness, Evolvability, and Accessibility in Linear Genetic Programming

Payne

Banzhaf

et al. 2011

Lecture Notes in Computer Science

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“…Although both the EO and GO treatments allow for large amounts of neutral mutation, which has been cited as a contributor to increased evolvability (Smith et al, 2001;Wagner, 2008), the GO treatment may allow for more connections to be constructed between the sensor and motor layers, or perhaps for more efficient, and less self-interfering, networks. This may in turn provide more raw materials for subsequent evolutionary change.…”

Section: Discussionmentioning

confidence: 99%

Epigenetic Operators and the Evolution of Physically Embodied Robots

et al. 2017

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The genetic operators (GOs) of recombination, mutation, and selection are commonly included in studies of evolution and evolvability, but they are not the only operators that can affect the genotype-to-phenotype (G → P) map and thus the outcomes of evolution. In this paper, we present experiments with an epigenetic operator (EO), interactive wiring of a circuit, alongside common GOs, investigating both epigenetic and GO effects on the evolution of both simulated and physically embodied Braitenberg-inspired robots. As a platform for our experiments, we built a system that encoded the genetics for the physical circuitry of the analog robots and made explicit rules for how that circuitry would be constructed; phenotypic expression consisted of the placement of wires to form the circuitry and thus govern robot behavior. We then varied the presence of gene interactions across populations of robots, studying how the EO-and its effects on G → P maps-affected the results of evolution over several generations. Additionally, a variant of these experiments was run in simulation to provide an independent test of the evolutionary impact of this EO. Our results demonstrate that robot populations with the EO had quantitatively different and potentially less adaptive evolution than populations without it. For example, selection increased the rate at which functional circuitry was lost in the population with the EO, compared to the population without it. In addition, in simulation, EO populations were significantly less fit than populations without it. More generally, results such as these demonstrate the interaction of genetic and EOs during evolution, suggesting the broad importance of including EOs in investigations of evolvability. To our knowledge, our work represents the first physically embodied EO to be used in the evolution of physically embodied robots.

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“…As I write, I know of no empirical work which demonstrates conclusively that the modulatory processes instantiated in GasNets can perform the crucial context-switching function that I have attributed to them. For while there is abundant evidence that such processes can mediate the transition between different phases of behaviour within the same task (Smith et al 2001), that is not the same thing as switching between contexts, which typically involves a re-evaluation of what the current task might be. Nevertheless, it is surely a thought worth pursuing that fluid functional and structural reconfiguration, driven in a bottom-up way by low-level neuro-chemical mechanisms, may be at the heart of the more complex capacity.…”

Section: Discussionmentioning

confidence: 99%