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46
Robust multi-cellular developmental design
- In GECCO ’07: Proc. of the 9th Annual Conference on Genetic and Evolutionary Computation
, 2007
"... This paper introduces a continuous model for Multi-cellular Developmental Design. The cells are fixed on a 2D grid and exchange ”chemicals ” with their neighbors during the growth process. The quantity of chemicals that a cell produces, as well as the differentiation value of the cell in the phenoty ..."
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Cited by 17 (6 self)
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This paper introduces a continuous model for Multi-cellular Developmental Design. The cells are fixed on a 2D grid and exchange ”chemicals ” with their neighbors during the growth process. The quantity of chemicals that a cell produces, as well as the differentiation value of the cell in the phenotype, are controlled by a Neural Network (the genotype) that takes as inputs the chemicals produced by the neighboring cells at the previous time step. In the proposed model, the number of iterations of the growth process is not pre-determined, but emerges during evolution: only organisms for which the growth process stabilizes give a phenotype (the stable state), others are declared nonviable. The optimization of the controller is done using the NEAT algorithm, that optimizes both the topology and the weights of the Neural Networks. Though each cell only receives local information from its neighbors, the experimental results of the proposed approach on the ’flags ’ problems (the phenotype must match a given 2D pattern) are almost as good as those of a direct regression approach using the same model with global information. Moreover, the resulting multi-cellular organisms exhibit almost perfect self-healing characteristics.
Simulating Evolution with a Computational Model of Embryogeny
, 2006
"... Natural evolution is an incredibly complex dynamical system which we are still grasping to understand. One could argue that the evolutionary search process itself is not the most important part of evolution, but rather it is the evolved representational structures and physical implementations of the ..."
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Natural evolution is an incredibly complex dynamical system which we are still grasping to understand. One could argue that the evolutionary search process itself is not the most important part of evolution, but rather it is the evolved representational structures and physical implementations of the genotype-phenotype mappings that make it so powerful. One such behaviour is embryogeny, the process by which genetic representations within cells control the development of a multi-cellular organism. Evolution of complex organisms is highly dependent upon biological mechanisms such as embryogeny, and this has fundamental consequences. The objective of this work is to investigate these consequences for a simulated evolutionary search process using a computational model of embryogeny. The model simulates the dynamics achieved within biological systems between the genetic representation of an individual and the mapping to its phenotypic representation. It is not an attempt to produce a biologically accurate or plausible model of the cell, growth or genetics. The model is used to create individuals which develop into multi-component patterns
On evolutionary design, embodiment, and artificial regulatory networks
- Embodied Artificial Intelligence
, 2004
"... Abstract. In this contribution we consider the idea that successful evolutionary design is best achieved in a networked system. We exemplify this thought by a discussion of artificial regulatory networks, a recently devised method to model natural genome-protein interactions. It is argued that emerg ..."
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Cited by 14 (0 self)
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Abstract. In this contribution we consider the idea that successful evolutionary design is best achieved in a networked system. We exemplify this thought by a discussion of artificial regulatory networks, a recently devised method to model natural genome-protein interactions. It is argued that emergent phenomena in nature require the existence of networks in order to become permanent. 1
Evolving Beyond Perfection: An Investigation of the Effects of LongTerm Evolution on Fractal Gene Regulatory Networks.2003c
- In Proc of Information Processing in Cells and Tissues (IPCAT
, 2003
"... This paper continues a theme of exploring algorithms based on principles of biological development for tasks such as pattern generation, machine learning and robot control. Previous work has investigated the use of genes expressed as fractal proteins to enable greater evolvability of gene regulatory ..."
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Cited by 14 (6 self)
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This paper continues a theme of exploring algorithms based on principles of biological development for tasks such as pattern generation, machine learning and robot control. Previous work has investigated the use of genes expressed as fractal proteins to enable greater evolvability of gene regulatory networks (GRNs). Here, the evolution of such GRNs is investigated further to determine whether evolution exhibits natural tendencies towards efficiency and graceful degradation of developmental programs. Experiments where “perfect ” GRNs are evolved for a further thousand generations without the addition of any further selection pressure, confirm this hypothesis. After further evolution, the perfect GRNs operate in a more efficient manner (using fewer proteins) and show an improved ability to function correctly with missing genes. When the algorithm is applied to applications (e.g. robot control) this equates to efficient and fault-tolerant controllers.
Adaptive Fractal Gene Regulatory Networks for Robot Control
- In Workshop on Regeneration and Learning in Developmental Systems in the Genetic and Evolutionary Computation Conference (GECCO 2004
, 2004
"... Abstract. Fractal proteins are an evolvable method of mapping genotype to phenotype through a developmental process, where genes are expressed into proteins comprised of subsets of the Mandelbrot Set. The resulting network of gene and protein interactions can be designed by evolution to produce spec ..."
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Cited by 12 (0 self)
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Abstract. Fractal proteins are an evolvable method of mapping genotype to phenotype through a developmental process, where genes are expressed into proteins comprised of subsets of the Mandelbrot Set. The resulting network of gene and protein interactions can be designed by evolution to produce specific patterns that in turn can be used to solve problems. In this paper, adaptive developmental programs, capable of developing different solutions in response to different signals from an environment, are investigated. Experiments show that such methods are highly effective in producing robot controllers that generate different movements in response to sensor inputs. 1
Facilitating evolutionary innovation by developmental modularity and variability
- In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO), ACM, 2009
"... Natural complex adaptive systems show many examples of self-organization and decentralization, such as pattern formation or swarm intelligence. Yet, only multicellular organisms possess the genuine architectural capabilities needed in many engineering application domains, from nanotechnologies to re ..."
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Cited by 11 (4 self)
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Natural complex adaptive systems show many examples of self-organization and decentralization, such as pattern formation or swarm intelligence. Yet, only multicellular organisms possess the genuine architectural capabilities needed in many engineering application domains, from nanotechnologies to reconfigurable and swarm robotics. Biological development thus offers an important paradigm for a new breed of “evo-devo ” computational systems. This work explores the evolutionary potential of an original multi-agent model of artificial embryogeny through differently parametrized simulations. It represents a rare attempt to integrate both self-organization and regulated architectures. Its aim is to illustrate how a developmental system, based on a truly indirect mapping from a modular genotype to a modular phenotype, can facilitate the generation of variations, thus structural innovation.
Evolving fractal gene regulatory networks for robot control
- In Proceedings of ECAL 2003
, 2003
"... Abstract. Fractal proteins are a new evolvable method of mapping genotype to phenotype through a developmental process, where genes are expressed into proteins comprised of subsets of the Mandelbrot Set. The resulting network of gene and protein interactions can be designed by evolution to produce s ..."
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Cited by 11 (2 self)
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Abstract. Fractal proteins are a new evolvable method of mapping genotype to phenotype through a developmental process, where genes are expressed into proteins comprised of subsets of the Mandelbrot Set. The resulting network of gene and protein interactions can be designed by evolution to produce specific patterns, that in turn can be used to solve problems. Here the use of fractal gene regulatory networks for learning a robot path through a series of obstacles is described. The results indicate the ability of this system to learn regularities in solutions and automatically create and use modules. 1
Epigenetic Tracking, a Method to Generate Arbitrary Shapes By Using Evolutionary-Developmental Techniques
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Robustness and the Halting Problem for Multi-Cellular Artificial Ontogeny
- "N/P" IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 2011
"... Most works in Multi-Cellular Artificial Ontogeny solve the halting problem by arbitrarily limiting the number of iterations of the developmental process. Hence, the trajectory of the developing organism in the phenotypic space is only required to come close to an accurate solution during a very shor ..."
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Cited by 6 (2 self)
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Most works in Multi-Cellular Artificial Ontogeny solve the halting problem by arbitrarily limiting the number of iterations of the developmental process. Hence, the trajectory of the developing organism in the phenotypic space is only required to come close to an accurate solution during a very short developmental period. Because of the well-known opportunism of evolution, there is indeed no reason for the organism to remain close to a good solution in other situations: if the development is continued after the limiting bound; if the environment is perturbed by some noise during the development; if the development takes place in different physical conditions. In order to increase the robustness of the solution against such hazards, a new stopping criterion for the developmental process is proposed, based on the stability of some internal energy of the organism during its development. Such adaptive stopping criterion biases evolution toward solutions in which robustness is an intrinsic property. Experimental results on different “French flag” problems demonstrate that enforcing stable developmental process makes it possible to produce solutions that not only accurately approximate the target shape, but also demonstrate near-perfect self-healing properties, as well as excellent generalization capabilities.
Evolution of robot controller using cartesian genetic programming
- In Proceedings of the 6th European Conference on Genetic Programming (EuroGP 2005), LNCS 3447
, 2005
"... Abstract. Cartesian Genetic Programming is a graph based representa-tion that has many benefits over traditional tree based methods, includ-ing bloat free evolution and faster evolution through neutral search. Here, an integer based version of the representation is applied to a traditional problem i ..."
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Cited by 6 (4 self)
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Abstract. Cartesian Genetic Programming is a graph based representa-tion that has many benefits over traditional tree based methods, includ-ing bloat free evolution and faster evolution through neutral search. Here, an integer based version of the representation is applied to a traditional problem in the field: evolving an obstacle avoiding robot controller. The technique is used to rapidly evolve controllers that work in a complex en-vironment and with a challenging robot design. The generalisation of the robot controllers in different environments is also demonstrated. A novel fitness function based on chemical gradients is presented as a means of improving evolvability in such tasks. 1
