241 research outputs found
"Going back to our roots": second generation biocomputing
Researchers in the field of biocomputing have, for many years, successfully
"harvested and exploited" the natural world for inspiration in developing
systems that are robust, adaptable and capable of generating novel and even
"creative" solutions to human-defined problems. However, in this position paper
we argue that the time has now come for a reassessment of how we exploit
biology to generate new computational systems. Previous solutions (the "first
generation" of biocomputing techniques), whilst reasonably effective, are crude
analogues of actual biological systems. We believe that a new, inherently
inter-disciplinary approach is needed for the development of the emerging
"second generation" of bio-inspired methods. This new modus operandi will
require much closer interaction between the engineering and life sciences
communities, as well as a bidirectional flow of concepts, applications and
expertise. We support our argument by examining, in this new light, three
existing areas of biocomputing (genetic programming, artificial immune systems
and evolvable hardware), as well as an emerging area (natural genetic
engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin
Phenotype Search Trajectory Networks for Linear Genetic Programming
Genotype-to-phenotype mappings translate genotypic variations such as
mutations into phenotypic changes. Neutrality is the observation that some
mutations do not lead to phenotypic changes. Studying the search trajectories
in genotypic and phenotypic spaces, especially through neutral mutations, helps
us to better understand the progression of evolution and its algorithmic
behaviour. In this study, we visualise the search trajectories of a genetic
programming system as graph-based models, where nodes are genotypes/phenotypes
and edges represent their mutational transitions. We also quantitatively
measure the characteristics of phenotypes including their genotypic abundance
(the requirement for neutrality) and Kolmogorov complexity. We connect these
quantified metrics with search trajectory visualisations, and find that more
complex phenotypes are under-represented by fewer genotypes and are harder for
evolution to discover. Less complex phenotypes, on the other hand, are
over-represented by genotypes, are easier to find, and frequently serve as
stepping-stones for evolution
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