Chaos as a Source of Complexity and Diversity in Evolution

Kunihiko Kaneko
<span title="">1993</span> <i title="MIT Press - Journals"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/trtzv54dg5e5lpy55iml6bgv6u" style="color: black;">Artificial Life</a> </i> &nbsp;
The relevance of chaos to evolution is discussed in the context of the origin and maintenance of diversity and complexity. Evolution to the edge of chaos is demonstrated in an imitation game. As an origin of diversity, dynamic clustering of identical chaotic elements, globally coupled each to the other, is briefly reviewed. The clustering is extended to nonlinear dynamics on hypercubic lattices, which enables us to construct a self-organizing genetic algorithm. A mechanism of maintenance of
more &raquo; ... rsity, "homeochaos," is given in an ecological system with interaction among many species. Homeochaos provides a dynamic stability sustained by high-dimensional weak chaos. A novel mechanism of cell differentiation is presented, based on dynamic clustering. Here, a new concept-"open chaos"-is proposed for the instability in a dynamical system with growing degrees of freedom. It is suggested that studies based on interacting chaotic elements can replace both top-down and bottom-up approaches. I Complexity, Diversity, and Emergence Why are we interested in the effort to create "lifelike answers can be diverse, but my interest in such artificial biology lies in the construction of systems exhibiting the emergence and maintenance of complexity and diversity, in order to understand the evolution of the complex "society" of life. This problem is not so trivial, indeed. It is often difficult to conclude that a system's emergent complexity is somewhat beyond that which would be expected on the basis of the nales explicitly implemented within a model [8] , Often, what people call "emergent" behavior comes from the lack of a full understanding of what is implied by the rules implemented in the model. In evolution, there is a stage of the emergence of novel features as well as a stage of slow-scale change of existing features. Gradual evolution after the emergence of a novel feature is often studied analytically with the use of stochastic differential equations, as, for example, is demonstrated by the neutral theory of evolution [21] . The "origin" of features, on the other hand, is often a difficult problem to solve analytically. The origins of life, eukaryotes, multicellular organism, germ-line segregation, and sex are examples of the emergence of such novel features. For such problems, we require a mechanism for how complex, higher-level behavior emerges from low-level interactions, without the implementation of explicit rules for such emergence. Such emergence, we believe, occurs through strong nonlinear interactions among the agents at the lower level. Nonlinear interaction among agents often leads to chaotic behavior,
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