Stefano Nichele

Genome parameters as information to forecast emergent developmental behaviors

In this paper we measure genomic properties in EvoDevo systems, to predict emergent phenotypic characteristic of artificial organisms. We describe and compare three parameters calculated out of the composition of the genome, to forecast the emergent behavior and structural properties of the developed organisms. The parameters are each calculated by including different genomic information. The genotypic information explored are: purely regulatory output, regulatory input and relative output considered independently and an overall parameter calculated out of genetic dependency properties. The goal of this work is to gain more knowledge on the relation between genotypes and the behavior of emergent phenotypes. Such knowledge will give information on genetic composition in relation to artificial developmental organisms, providing guidelines for construction of EvoDevo systems. A minimalistic developmental system based on Cellular Automata is chosen in the experimental work.

On the correlations between developmental diversity and genomic composition

In this work we target to measure genomic properties in EvoDevo systems as to predict phenotypic properties related to the emergence of artificial organisms. We propose a measurement, λ d, based on the composition of the genome, that can give prediction on how the emerging organism will develop. The experimental approach uses a minimalistic developmental model. The result show that the parameter λ d can predict phenotypic properties. The aim of introducing a parameter like λ d is to get more knowledge on the relation between genomic properties and phenotypic properties of developing organisms.

Trajectories and attractors as specification for the evolution of behaviour in cellular automata

In this paper evolvability of uniform and nonuniform cellular automata is investigated in relation to the level of detail in the behavioural description. An experimental approach was taken to test how increased information, from only initial and final state to an approach including intervals of interest in the state space, influenced the evolutionary process. The results are promising as it seems that a more detailed specification of the behaviour, i.e. trajectory, was not annihilating regarding evolvability. The presented work is part of a larger goal of applying artificial evolution and development to achieve the goal of designing cellular machines that can be applied to complex computation and modelling. The specification of the sought problems for such machines is often of a nature that includes incomplete knowledge of the behaviour.

Computational matter: evolving computational functions in nanoscale materials

Natural evolution has been manipulating the properties of proteins for billions of years. This ‘design process’ is completely different to conventional human design which assembles well-understood smaller parts in a highly principled way. In evolution-in-materio (EIM), researchers use evolutionary algorithms to define configurations and magnitudes of physical variables (e.g. voltages) which are applied to material systems so that they carry out useful computation. One of the advantages of this is that artificial evolution can exploit physical effects that are either too complex to understand or hitherto unknown. An EU funded project in Unconventional Computation called NASCENCE: Nanoscale Engineering of Novel Computation using Evolution, has the aim to model, understand and exploit the behaviour of evolved configurations of nanosystems (e.g. networks of nanoparticles, carbon nanotubes, liquid crystals) to solve computational problems. The project showed that it is possible to use materials to help find solutions to a number of well-known computational problems (e.g. TSP, Bin-packing, Logic gates, etc.).

An Investigation of Square Waves for Evolution in Carbon Nanotubes Material

Materials suitable to perform computation make use of evolved configuration signals which specify how the material samples are to operate. The choice of which input and configuration parameters to manipulate obviously impacts the potential of the computational device that emerges. As such, a key challenge is to understand which parameters are better suited to exploit the underlying physical properties of the chosen material. In this paper we focus on the usage of square voltage waves as such manipulation parameters for carbon nanotubes/polymer nanocomposites. The choice of input parameters influence the reachable search space, which may be critical for any kind of evolved computational task. We provide common measurements such as power spectrum and phase plots, taken with the the Mecobo platform, a custombuilt board for evolution-in-materio. In addition, an initial investigation is carried out, which links the frequency of square waves to comparability of the output from the material, while also showing differences in the material’s physical parameters. Observing the behaviour of materials under varying inputs allows macroscopic modelling of pin-to-pin characteristics with simple RC circuits. Finally, SPICE is used to provide a rudamentary simulation of the observed properties of the material. This simulation models the per-pin behaviours, and also shows that an instance of the traveling-salesmanproblem can be solved with a simple randomly generated cloud of resistors.

Evolutionary growth of genome representations on artificial cellular organisms with indirect encodings

Evolutionary design targets systems of continuously increasing complexity. Thus, indirect developmental mappings are often a necessity. Varying the amount of genotype information changes the cardinality of the mapping, which in turn affects the developmental process. An open question is how to find the genotype size and representation in which a developmental solution would fit. A restricted pool of genes may not be large enough to encode a solution or may need complex heuristics to find a realistic size. On the other hand, using the whole set of possible regulatory combinations may be intractable. In nature, the genomes of biological organisms are not fixed in size; they slowly evolve and acquire new genes by random gene duplications. Such incremental growth of genome information can be beneficial also in the artificial domain. For an evolutionary and developmental (evo-devo) system based on cellular automata, we investigate an incremental evolutionary growth of genomes without any a priori knowledge on the necessary genotype size. Evolution starts with simple solutions in a low-dimensional space and incrementally increases the genotype complexity by means of gene duplication, allowing the evolution of scalable genomes that are able to adapt genetic information content while compactness and efficiency are retained. The results are consistent when the target phenotypic complexity, the geometry size, and the number of cell states are scaled up.

Evolution of Incremental Complex Behavior on Cellular Machines

Complex multi-cellular organisms are the result of evolution over billions of years. Their ability to reproduce and survive through adaptation to selection pressure did not happen suddenly; it required gradual genome evolution that eventually led to an increased emergent complexity. In this paper we investigate the emergence of complexity in cellular machines, using two different evolutionary strategies. The first approach is a conventional genetic algorithm, where the target is the maximum complexity. This is compared to an incremental approach, where complexity is gradually evolved. We show that an incremental methodology could be better suited to help evolution to discover complex emergent behaviors. We also propose the usage of a genome parameter to detect the behavioral regime. The parameter may indicate if the evolving genomes are likely to be able to achieve more complex behaviors, giving information on the evolvability of the system. The experimental model used herein is based on 2-dimensional cellular automata. We show that the incremental approach is promising when evolution targets an increase of complexity.

Investigation of Genome Parameters and Sub-Transitions to Guide Evolution of Artificial Cellular Organisms

Artificial multi-cellular organisms develop from a single zygote to complex morphologies, following the instructions encoded in their genomes. Small genome mutations can result in very different developed phenotypes. In this paper we investigate how to exploit genotype information in order to guide evolution towards favorable areas of the phenotype solution space, where the sought emergent behavior is more likely to be found. Lambda genome parameter, with its ability to discriminate different developmental behaviors, is incorporated into the fitness function and used as a discriminating factor for genetic distance, to keep resulting phenotype’s developmental behavior close by and encourage beneficial mutations that yield adaptive evolution. Genome activation patterns are detected and grouped into genome parameter sub-transitions. Different sub-transitions are investigated as simple genome parameters, or composed to integrate several genome properties into a more exhaustive composite parameter. The experimental model used herein is based on 2-dimensional cellular automata.

Measuring Phenotypic Structural Complexity of Artificial Cellular Organisms

Artificial multi-cellular organisms develop from a single zygote to different structures and shapes, some simple, some complex. Such phenotypic structural complexity is the result of morphogenesis, where cells grow and differentiate according to the information encoded in the genome. In this paper we investigate the structural complexity of artificial cellular organisms at phenotypic level, in order to understand if genome information could be used to predict the emergent structural complexity. Our measure of structural complexity is based on the theory of Kolmogorov complexity and approximations. We relate the Lambda parameter, with its ability to detect different behavioral regimes, to the calculated structural complexity. It is shown that the easily computable Lempel-Ziv complexity approximation has a good ability to discriminate emergent structural complexity, thus providing a measurement that can be related to a genome parameter for estimation of the developed organism’s phenotypic complexity. The experimental model used herein is based on 1D, 2D and 3D Cellular Automata.

Discrete dynamics of cellular machines: specification and interpretation

This paper presents research on discrete dynamics of cellular machines, their specification and interpretation. It gives an overview of the fundamental issues related to the classification of Cellular Automata (CA) classes. In particular, the possible locations of various CA capable to achieve different degrees of complex behaviors are described. This work is mainly focused on the correlation between CA behavior and cellular regulative properties. A possible minimalistic experimental setup is presented, together with some preliminary results and ideas that can be investigated in future work.