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In this chapter we discuss the challenge provided by the problem of evolving large amounts of computer code via Genetic Programming. We argue that the problem is analogous to what Nature had to face when moving to multi-cellular life. We propose to look at developmental processes and there mechanisms to come up with solutions for this “challenge of complexity” in Genetic Programming.
In this chapter we discuss the challenge provided by the problem of evolving large amounts of computer code via Genetic Programming. We argue that the problem is analogous to what Nature had to face when moving to multi-cellular life. We propose to look at developmental processes and there mechanisms to come up with solutions for this “challenge of complexity” in Genetic Programming.
Today’s software is brittle. A tiny corruption in an executable will normally result in terminal failure of that program. But nature does not seem to suffer from the same problems. A multicellular organism, its genes evolved and developed, shows graceful degradation: should it be damaged, it is designed to continue to work. This paper describes an investigation into software with the same properties...
This contribution moves in the direction of answering some general questions about the most effective and useful ways of modelling bioprocesses. We investigate the characteristics of models that are good at extrapolating. We trained three fully predictive models with different representational structures (differential equations, differential equations with inheritance of rates and a network of reactions)...
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