TY - CHAP

T1 - Evolving Uniform and Non-Uniform Cellular Automata Networks

AU - Sipper, Moshe

PY - 1997/6

Y1 - 1997/6

N2 - Natural evolution has "created" many parallel cellular systems, in which
emergent computation gives rise to impressive computational
capabilities. In recent years we are witness to a rapidly growing
interest in such complex adaptive systems, addressing, among others, the
major problem of designing them to exhibit a specific behavior or solve
a given problem. One possible approach, which we explore in this paper,
is to employ artificial evolution. The systems studied are based on the
cellular automata (CA) model, where a regular grid of cells is updated
synchronously in discrete time steps, according to a local, identical
interaction rule. We first present the application of a standard genetic
algorithm to the evolution of CAs to perform two non-trivial
computational tasks, density and synchronization, showing that
high-performance systems can be attained. The evolutionary process as
well as the resulting emergent computation are then discussed. Next we
study two generalizations of the CA model, the first consisting of
non-uniform CAs, where cellular rules need not be identical for all
cells. Introducing the cellular programming evolutionary algorithm, we
apply it to six computational tasks, demonstrating that high-performance
systems can be evolved. The second generalization involves non-standard,
evolving connectivity architectures, where we demonstrate that yet
better systems can be attained. Evolving, cellular systems hold
potential both scientifically, as vehicles for studying phenomena of
interest in areas such as complex adaptive systems and artificial life,
as well as practically, showing a range of potential future applications
ensuing the construction of adaptive systems, and in particular
`evolving ware,' evolware.

AB - Natural evolution has "created" many parallel cellular systems, in which
emergent computation gives rise to impressive computational
capabilities. In recent years we are witness to a rapidly growing
interest in such complex adaptive systems, addressing, among others, the
major problem of designing them to exhibit a specific behavior or solve
a given problem. One possible approach, which we explore in this paper,
is to employ artificial evolution. The systems studied are based on the
cellular automata (CA) model, where a regular grid of cells is updated
synchronously in discrete time steps, according to a local, identical
interaction rule. We first present the application of a standard genetic
algorithm to the evolution of CAs to perform two non-trivial
computational tasks, density and synchronization, showing that
high-performance systems can be attained. The evolutionary process as
well as the resulting emergent computation are then discussed. Next we
study two generalizations of the CA model, the first consisting of
non-uniform CAs, where cellular rules need not be identical for all
cells. Introducing the cellular programming evolutionary algorithm, we
apply it to six computational tasks, demonstrating that high-performance
systems can be evolved. The second generalization involves non-standard,
evolving connectivity architectures, where we demonstrate that yet
better systems can be attained. Evolving, cellular systems hold
potential both scientifically, as vehicles for studying phenomena of
interest in areas such as complex adaptive systems and artificial life,
as well as practically, showing a range of potential future applications
ensuing the construction of adaptive systems, and in particular
`evolving ware,' evolware.

U2 - 10.1142/9789812819444_0006

DO - 10.1142/9789812819444_0006

M3 - Chapter

SN - 9789810231811

SN - 9789810231828

VL - 5

T3 - Annual Reviews of Computational Physics V

SP - 243

EP - 285

BT - Annual Reviews of Computational Physics V

A2 - Stauffer, Dietrich

PB - World Scientific

ER -