This is part of the RobotsprogrammeBack
A system composed of multiple interacting components is capable of responding to contextual information and can produce a higher range of non-linear responses to stimuli compared to a modular system with a low degree of component interaction. However, the fitness landscape of highly integrated systems is more rugged indicating that such systems are likely to be less evolvable.
In this work we use an artificial life simulation to investigate whether the evolvability of highly integrated systems can be improved if the level of integration between the systemís components is under evolutionary control. When evolving our multi-component system we discover that the level of integration very quickly falls to virtually zero reducing the ruggedness of the landscape and making it nearly neutral. This allows the evolving population to explore the genome space without getting stuck on local optima. The components then integrate and the evolving population settles on the global optimum.
This work is unique because the presented problem requires the evolving system to be fully integrated in order to solve it and as such the decreased ruggedness and near neutrality are not a permanent feature of the landscape but rather a property which is temporarily manipulated and exploited by the evolving population.
A new reduction from SAT to n-Partite Graphs.