Recent years have seen the widespread application of Natural
Computing algorithms (broadly defined in this context as computer
algorithms whose design draws inspiration from phenomena in the
natural world) for the purposes of financial modelling and
optimisation. A related stream of work has also seen the
application of learning mechanisms drawn from Natural Computing
algorithms for the purposes of agent-based modelling in finance and
economics. In this book we have collected a series of chapters
which illustrate these two faces of Natural Computing. The first
part of the book illustrates how algorithms inspired by the natural
world can be used as problem solvers to uncover and optimise
financial models. The second part of the book examines a number
agent-based simulations of financial systems.
This book follows on from Natural Computing in Computational
Finance (Volume 100 in Springer's Studies in Computational
Intelligence series) which in turn arose from the success of EvoFIN
2007, the very first European Workshop on Evolutionary Computation
in Finance & Economics held in Valencia, Spain in April
2007.
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