Harnessing morphogenesis

Jakobi, Nick

doi:10.1016/b978-012428765-5/50054-2

Cited by 36 publications

(36 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All the other researchers listed in Table I made use of the graded chemicals in their systems. In the earlier models, Kitano and Jakobi used a complex model of response strategies that may have overcomplicated the system [54], [55]. Eggenberger's work that followed Kitano and Jakobi succeeded in providing impressive results, and this model had provided a much simpler response strategy [13].…”

Section: B Cell Signalingmentioning

confidence: 99%

“…5 Although more biologically inspired, a micro-model developmental system does not necessarily model biological development perfectly. In fact there is much work in this type of artificial development with diverse design constraints, those that model biology closely [1], [13], [54]- [56], those that aim to model biological development in a simplistic fashion [19], [57], [58], and models that are "in-between" [3], [4], [9], [31], [59]. Mimicking biology closely should provide a developmental system with high evolvability [30], [60], whereas a simplistic model would reduce the number of complicated processes that exist in biological development, reducing simulation times drastically.…”

Section: B Models Of Artificial Developmentmentioning

confidence: 99%

See 1 more Smart Citation

An Investigation of the Importance of Mechanisms and Parameters in a Multicellular Developmental System

Kuyucu

Trefzer

Miller

et al. 2011

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

Abstract-Multicellular organisms in biology possess invaluable characteristics, which artificial systems in engineering lack, such as adaptivity and robustness. Modeling biologically inspired multicellular developmental systems in evolutionary computation have been considered for a number of years, and there exist many developmental simulations that capture multicellular characteristics of biological organisms. However, the relative importance of many mechanisms in such models is still poorly understood. This paper undertakes a detailed investigation of the importance of many mechanisms and parameters on the organizational behavior of a gene regulatory network based artificial developmental system via classical pattern matching experiments. The work leads to an improved understanding of artificial multicellular development, which will assist in its utilization in the application of evolutionary computation. It may also provide a better understanding of mechanisms of biological development.

show abstract

Section: B Cell Signalingmentioning

confidence: 99%

Section: B Models Of Artificial Developmentmentioning

confidence: 99%

An Investigation of the Importance of Mechanisms and Parameters in a Multicellular Developmental System

Kuyucu

Trefzer

Miller

et al. 2011

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

show abstract

“…In some works, models of GRNs are evolved for making mathematical output functions such as sinusoid, exponential and sigmoid [7]. Some researchers have designed GRN systems for developing neural networks for controlling robots [8,9,10] or specifying the morphology of 3D organisms [11]. Also, GRN models have been used to develop the morphology of robots as well as their neural network controllers [12].…”

Section: Related Workmentioning

confidence: 99%

Fractal Gene Regulatory Networks for Robust Locomotion Control of Modular Robots

Zahadat

Christensen

Schultz

et al. 2010

From Animals to Animats 11

View full text Add to dashboard Cite

Abstract. Designing controllers for modular robots is difficult due to the distributed and dynamic nature of the robots. In this paper fractal gene regulatory networks are evolved to control modular robots in a distributed way. Experiments with different morphologies of modular robot are performed and the results show good performance compared to previous results achieved using learning methods. Furthermore, some experiments are performed to investigate evolvability of the achieved solutions in the case of module failure and it is shown that the system is capable of come up with new effective solutions.

show abstract

“…A crucial aspect of cellular development is gene expression, with genes forming networks of complex interactions termed Genetic Regulatory Networks (GRNs). Numerous notable GRN models have been presented for the purpose of ANN evolution, e.g., [8], [9], [10]; some recent models employ evolved programs [11] or recurrent ANNs [12] to satisfy this role. A common issue here is the complexity involved, which implies not only a considerable computational cost, but also a general difficulty in analyzing such systems (and rarity; we are not aware of any detailed studies).…”

Section: B Artificial Embryogenymentioning

confidence: 99%

Graph design by graph grammar evolution

Luerssen

Powers

2007

2007 IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

Abstract-Determining the optimal topology of a graph is pertinent to many domains, as graphs can be used to model a variety of systems. Evolutionary algorithms constitute a popular optimization method, but scalability is a concern with larger graph designs. Generative representation schemes, often inspired by biological development, seek to address this by facilitating the discovery and reuse of design dependencies and allowing for adaptable exploration strategies. We present a novel developmental method for optimizing graphs that is based on the notion of directly evolving a hypergraph grammar from which a population of graphs can be derived. A multi-objective design system is established and evaluated on problems from three domains: symbolic regression, circuit design, and neural control. The observed performance compares favorably with existing methods, and extensive reuse of subgraphs contributes to the efficient representation of solutions. Constraints can also be placed on the type of explored graph spaces, ranging from tree to pseudograph. We show that more compact solutions are attainable in less constrained spaces, although convergence typically improves with more constrained designs.

show abstract

Harnessing morphogenesis

Cited by 36 publications

References 6 publications

An Investigation of the Importance of Mechanisms and Parameters in a Multicellular Developmental System

An Investigation of the Importance of Mechanisms and Parameters in a Multicellular Developmental System

Fractal Gene Regulatory Networks for Robust Locomotion Control of Modular Robots

Graph design by graph grammar evolution

Contact Info

Product

Resources

About