  Genetic Algorithms (GA) are a powerful set of tools for search and
optimization that mimic the process of natural selection, and have been used
successfully in a wide variety of problems, including evolving neural networks
to solve cognitive tasks. Despite their success, GAs sometimes fail to locate
the highest peaks of the fitness landscape, in particular if the landscape is
rugged and contains multiple peaks. Reaching distant and higher peaks is
difficult because valleys need to be crossed, in a process that (at least
temporarily) runs against the fitness maximization objective. Here we propose
and test a number of information-theoretic (as well as network-based) measures
that can be used in conjunction with a fitness maximization objective
(so-called ``neuro-correlates") to evolve neural controllers for two widely
different tasks: a behavioral task that requires information integration, and a
cognitive task that requires memory and logic. We find that judiciously chosen
neuro-correlates can significantly aid GAs to find the highest peaks.
