Enhancing evolvability with mutation buffering mediated through multiple weak interactions.
The evolutionary adaptability of a system is dependent on three organizational properties, self-organizing dynamics that are hierarchically organized, component redundancy, and multiple weak interactions [Towards high evolvability dynamics, in: G. van de Vijver, S. Salthe, M. Delpos (Eds.), Evolutionary Systems, Kluwer Academic Publishers, Dordrecht, 1998, pp. 147-169]. This study reports on the use of the dual dynamics network model as an aid in understanding the role multiple weak interactions play in enhancing evolutionary adaptability. Dual dynamics networks are self-organizing systems that consist of simple components that change local state due to the coupled influences from connected components exerting strong discrete decision-making influences and from groups of components exerting multiple weak influences [J. Theor. Biol. 193 (1998) 287]. The dual dynamics model has been enhanced to support investigations of properties relevant to a system's capacity for evolvability, such as structure-function relationships, neutrality, adaptive tolerance, and evolutionary search performance. Three network types are investigated, each utilizing a different method of coupling strong and weak influences. The results demonstrate that the manner of coupling multiple weak interactions into the systems dynamics significantly affects the structure-function maps and the consequent evolvability characteristics. Specifically it is found that a form of coupling, denoted as linear modulation, enhances evolutionary adaptability. Linear modulation coupling requires that the weak interactions be integrated with strong interactions in a manner that implies a linear ordered relation between the possible state values of the components of the systems. When coupling functions that do not imply such an ordering of local state values are used, evolutionary adaptability is decreased.
Volkert
L Gwenn
LG
Department of Computer Science, Kent State University, Kent, OH, 44242-0001, USA. gwv@cs.kent.edu
D000222:Adaptation, Physiological / Q000235:genetics*; D000818:Animals; D005075:Biological Evolution; D003198:Computer Simulation; D004252:DNA Mutational Analysis / Q000379:methods; D014644:Genetic Variation; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D009154:Mutation / Q000235:genetics*; D010641:Phenotype; D011157:Population Dynamics; D013045:Species Specificity