Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A robust evolutionary framework for multi-objective optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Realising emergent image preprocessing tasks in cellular-automaton-alike massively parallel hardware
International Journal of Parallel, Emergent and Distributed Systems - Emergent Computation
Emergence in organic computing systems: discussion of a controversial concept
ATC'06 Proceedings of the Third international conference on Autonomic and Trusted Computing
Emergence versus self-organisation: different concepts but promising when combined
Engineering Self-Organising Systems
Revising the trade-off between the number of agents and agent intelligence
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
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Emergence is an important and promising scientific topic today because it offers benefits that can not be achieved by classic means. But it is often challenging to control emergence and to find correct local rules that create desired global behavior. It especially becomes difficult if the search space representing the problem that has to be optimized is not continuous/linear. One solution to that problem is evolution. This paper shows that the use of Genetic Algorithms is feasible for such problems by the example of the Creatures' Exploration Problem in which agents shall visit all non-blocked cells in a grid. Different amounts of agents and states per agent are evolved and statistically compared. It shows that neither a single extension of agent capabilities nor sole increase of agent numbers provides the best performance. The results hint that a mixture of both should be used instead.