This paper carries out the study of natural networks (genetic, chemical and immune) evolving according to two levels of change called dynamics and metadynamics. The dynamics is the evolution in time of the concentration of the units currently present in the networks: the genetic, the molecular or the immune species. Their concentration evolves as a function of their network interaction with the other units. However, this evolution is also a function of their "exogenous" fitness so that the fitter units should in principle grow faster than the others. The metadynamics is the only way for innovation, and amounts to the generation of new units on the basis of the genetic or chemical materials constituting the units existing so far in the network. This metadynamics, indirectly subject to the exogenous pressure, tends to selectively favor units that are easier to produce from the existing ones. For instance, the genetic recombination of two species could occur between species presenting particular similar properties and thus generating new species merging these properties. This metadynamics also greatly influences the concentration of the units present in the network. The paper experimentally shows, on chemical, genetic and immune networks, that the interaction between these two levels of change, together with the intricate balance between the "exogenous" and the "network endogenous" selective drift, can induce a hard-to-predict concentration profile, subject to discontinuous changes.
[1]
Hugues Bersini.
Design Patterns for an Object-Oriented Computational Chemistry
,
1999,
ECAL.
[2]
Hugues Bersini,et al.
The Endogenous Double Plasticity of the Immune Network and the Inspiration to be drawn for Engineering Artifacts
,
1993
.
[3]
Stuart A. Kauffman,et al.
The origins of order
,
1993
.
[4]
Hugues Bersini,et al.
Immune Idiotypic Network
,
1996
.
[5]
David E. Goldberg,et al.
Genetic Algorithms in Search Optimization and Machine Learning
,
1988
.
[6]
M. Eigen,et al.
Molecular quasi-species.
,
1988
.
[7]
D. Dasgupta.
Artificial Immune Systems and Their Applications
,
1998,
Springer Berlin Heidelberg.
[8]
M. Muir.
Physical Chemistry
,
1888,
Nature.
[9]
F. Varela,et al.
Development of an idiotypic network in shape space.
,
1994,
Journal of theoretical biology.
[10]
Hugues Bersini.
Reaction Mechanisms in the OO Chemistry
,
2000
.
[11]
Hugues Bersini,et al.
Chemical Crossover
,
2000,
GECCO.
[12]
John R. Koza,et al.
Genetic programming - on the programming of computers by means of natural selection
,
1993,
Complex adaptive systems.
[13]
Loretta L. Jones,et al.
Chemical Principles: The Quest for Insight
,
1999
.