René Doursat
 PhD, Habil.

Research Scientist & Fmr. Director

Complex Systems Institute, Paris Ile-de-France (ISC-PIF)
French National Center for Scientific Research (CNRS)

Research Unit UPS3611

113, rue Nationale
75013 Paris, France


Edited Proceedings

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Evolution & Ecology  
Agent-based, genetic programming or grammar models of population dynamics combining the short and long time scales of individual interactions and evolution
In this section of my research, I have collaborated to various artificial life studies, which focused on the level of large populations of individuals and examined their collective evolution. Although there are no "morphogenetic engineering" concerns per se, individuals are still internally sophisticated, as they can contain a long genomic sequence, an intricate genetic program, or a set of generative (rewrite) rules giving rise to complex morphologies. ← Less
EVOSPACE – Evolutionary Dynamics and Speciation in Space  
A spatially explicit model of endogenous speciation in the absence of environmental constraints  
A commonly held view in evolutionary biology is that speciation, i.e., the emergence of genetically distinct and reproductively incompatible subpopulations, is driven by the external environment. Guy Hoelzer, Rich Drewes and myself have developed a spatially explicit model of a biological population to study the emergence of spatial and temporal patterns of genetic diversity in the absence of such constraints.
Speciation is usually thought to be either caused by geographical boundaries ("allopatric" speciation from mountains, rivers, islands, etc.; Fig. a, left) or pre-existing environmental inhomogeneities ("parapatric" or "sympatric" speciation from unequal climate or resource distribution; Fig. a, right). We propose a 2-D cellular automata model showing that an initially homogeneous population might spontaneously segment into different species through sheer isolation by distance, without the need for natural divisions.

Each location on the lattice contains several individuals that follow simple rules of reproduction, mutation and migratory dispersal. At each generation, they randomly move within a certain distance, mate with other individuals in their neighborhood and produce offspring, which can also mutate with a small probability. Their diploid genomes comprise two pairs of 1000-base chromosomes assorted independently during mating (Fig. b). Due to an "outbreeding depression" effect, offspring viability is also bound to decrease with the genetic distance between parents.

Our simulations show that in a certain domain of parameters a phase transition revealing spontaneous segmentation and clustering occurs in the network. This inherent tendency toward spatial self-organization is analogous to Turing pattern formation on the population scale (Fig. c).

To explore this pattern of population diversity we plot a frequency histogram of the genetic difference between pairs of randomly chosen individuals, or "mismatch distribution" (Fig. d). While a well-mixed version of the model exhibits a relatively stable and unimodal distribution, a drifting multimodal distribution emerges when the dispersal of individuals is limited to short distances. The long-term persistence of such diverging subpopulations is the essence of biological speciation. ← Less


Hoelzer, G., Drewes, R., Meier, J. & Doursat, R. (2008) Isolation-by-distance and outbreeding depression are sufficient to drive parapatric speciation in the absence of environmental influences. PLoS Computational Biology 4(7): e1000126. PAPER

DIVPLANT – Emergent Diversity in Communities of Virtual Plants  
A model of spatial population dynamics combining L-systems, genetic expression, biologically inspired mutations, and open-ended evolution  
This part of José David Fernández's PhD, which I co-directed with his main advisor Francisco Vico, examines the formation, evolution, and diversity dynamics of a community of virtual plants through a new individual-centered model at multiple scales: genetic, developmental, and physiological. It is an original attempt to combine development, evolution, and population dynamics (from multi-agent interactions) into one comprehensive, yet simple model.
Understanding the dynamics of biodiversity has become an important line of research in theoretical ecology and, in particular, conservation biology. However, studying the evolution of ecological communities under traditional modeling approaches based on differential calculus requires species' characteristics to be predefined, which limits the generality of the results.

An alternative but less standardized methodology relies on intensive computer simulation of evolving communities made of simple, explicitly described individuals. Compared to analytical frameworks, two major properties are distinctive of the agent-based computational approach: emergence and adaptation. New properties at higher scales, which were not explicitly included in the model, appear in the system as the individuals interact. Simultaneously, the community can also evolve and adapt to new environmental conditions, therefore broadening the scope of possible experiments and testable hypotheses.

In this work, we focus on an especially puzzling aspect of evolution: diversification. We simulate a virtual community of plants to study the emergence and dynamics of genomic and phenotypic variation during evolution. To model plants, we use rewriting systems, a family of formal methods widely investigated in theoretical computer science and capable of encoding and generating complex structures. In this formal framework, parts of an initial object, generally strings of symbols, are iteratively replaced by other parts, generally longer string segments, following rewrite rules.
We observe that our simulated plants evolve increasingly elaborate canopies, which are capable of intercepting ever greater amounts of light. Generated morphologies vary from the simplest one-branch structure of promoter plants to a complex arborization of several hundred thousand branches in highly evolved variants. On the population scale, the heterogeneous spatial structuration of the plant community at each generation depends solely on the evolution of its component plants.

Using this virtual data, the morphologies and the dynamics of diversity production were analyzed by various statistical methods, based on genotypic and phenotypic distance metrics. The results demonstrate that diversity can spontaneously emerge in a community of mutually interacting individuals under the influence of specific environmental conditions. ← Less


Fernández, J. D., Lobo, D., Martín, G. M., Doursat, R. & Vico, F. J. (2012) Emergent diversity in an open-ended evolving virtual community. Artificial Life 18(2): 199-222. PAPER

HETCA – Long-Term Evolutionary Dynamics in Heterogeneous Cellular Automata  
Exploring a new class of cellular automata capable of longterm phenotypic dynamics, including a high level of variance and behavioral diversity.  
In this original work by my MSc student David Medernach, co-supervised by Taras Kowaliw, we study open-ended evolution by analyzing heterogeneous cellular automata (HetCA). The model involves cells that have an "age", "decay", and "quiescence", a transition function based on genetic programming, and transfer between adjacent cells. These changes convert a CA system into a new kind of "ecosystem", where different genomes compete for existence.
A major motivation for work in artificial life is to create open-ended evolution, or systems in which novel artifacts are continuously produced. The best known example is Ray's Tierra world, where there is competition between replicating computer programs in a virtual machine. Later, another evolutionary system by Adami, called Avida, became popular and was extended in many directions. High-level systems exist as well, where agents execute complex predefined functions, such as eating or fighting in Yaeger's Polyworld.

In all these cases, there is no particular specified goal: interesting and sometimes unexpected phenomena emerge from the interaction of individuals and their environment. An important insight into the majority of such successful systems is that they involve ecosystemic interactions.

Our new artificial life model is based on a discrete dynamical systems framework. We have extended classical 2D cellular automata (CA), our chief modification being the allowance for heterogeneous transition functions. The cumulative effect of these features is to create an evolving ecosystem of competing cell colonies.

To evaluate our new model and demonstrate its open-endedness, we define a measure of phenotypic diversity on the space of cellular automata. Our results show that HetCA is capable of supporting long-term dynamical behaviour and phenotypic changes, not readily achieved in control homogeneous CA, such as the Game of Life. In particular, we exhibit examples of the strategies discovered by our system, which are characterized by the emergence of competitive behaviour. ← Less


Ray (1991) An approach to the synthesis of life. ALife II.

Yaeger (1993) Polyworld. ALife III. SFI Series.

Adami & Brown (1994) Avida. ALife IV. MIT Press.


Medernach, D., Carrignon, S., Doursat, R., Kowaliw, T., Fitzgerald, J. & Ryan, C. (2016) Evolution of heterogeneous cellular automata in fluctuating environments. Proceedings of the Artificial Life Conference 2016 (ALife XV), July 4-8, 2016, Cancun, Mexico; C. Gershenson et al., eds.: pp. 216-223. MIT Press, ISBN 978-0-262-33936-0. PAPER

Medernach, D., Kowaliw, T., Ryan, C. & Doursat, R. (2013) Long-term evolutionary dynamics in heterogeneous cellular automata. "Artificial Life" Track (ALIFE 2013), in Proceedings of the 15th International Genetic and Evolutionary Computation Conference (GECCO 2013), July 6-10, 2013, Amsterdam, The Netherlands; C. Blum et al., eds.: pp. 231-238. ACM, ISBN 978-1-4503-1963-8. PAPER