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Artificial and natural instances of networks are
ubiquitous, and the problem of determining the
optimal topology of a network is of practical value
to many domains. Evolutionary algorithms constitute a
well-established optimisation method, but they scale
poorly if applied to the combinatorial explosion of
possible network topologies. Generative
representation schemes aim to overcome this problem
by facilitating the discovery and reuse of design
dependencies and allowing for adaptable exploration
strategies. This book seeks to define a simple yet
universally applicable and scalable method for
evolving graphs and networks. A number of
contributions are made in this regard. We establish
the notion of directly evolving a graph grammar from
which a population of networks can be derived.
Compact cellular productions that form a hypergraph
grammar are optimised by a novel multi-objective
evolutionary design system. A series of empirical
investigations are then carried out to gain a better
understanding of graph grammar evolution.
ubiquitous, and the problem of determining the
optimal topology of a network is of practical value
to many domains. Evolutionary algorithms constitute a
well-established optimisation method, but they scale
poorly if applied to the combinatorial explosion of
possible network topologies. Generative
representation schemes aim to overcome this problem
by facilitating the discovery and reuse of design
dependencies and allowing for adaptable exploration
strategies. This book seeks to define a simple yet
universally applicable and scalable method for
evolving graphs and networks. A number of
contributions are made in this regard. We establish
the notion of directly evolving a graph grammar from
which a population of networks can be derived.
Compact cellular productions that form a hypergraph
grammar are optimised by a novel multi-objective
evolutionary design system. A series of empirical
investigations are then carried out to gain a better
understanding of graph grammar evolution.