Scalable learning in genetic programming using automatic function definition
Advances in genetic programming
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A new kind of science
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
On the Effectiveness of Evolution Compared to Time-Consuming Full Search of Optimal 6-State Automata
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Evaluating the evolvability of emergent agents with different numbers of states
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Emergence in organic computing systems: discussion of a controversial concept
ATC'06 Proceedings of the Third international conference on Autonomic and Trusted Computing
Optimal 6-state algorithms for the behavior of several moving creatures
ACRI'06 Proceedings of the 7th international conference on Cellular Automata for Research and Industry
Emergence versus self-organisation: different concepts but promising when combined
Engineering Self-Organising Systems
Using the experimental method to produce reliable self-organised systems
Engineering Self-Organising Systems
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
Emergent agents are a promising approach to handle complex systems. Agent intelligence is thereby either defined by the number of states and the state transition function or the length of their steering programs. Evolution has shown to be successful in creating desired behaviors for such agents. Genetic algorithms have been used to find agents with fixed numbers of states and genetic programming is able to balance between the steering program length and the costs for longer programs. This paper extends previous work by further discussing the relationship between either using more agents with less intelligence or using fewer agents with higher intelligence. Therefore, the Creatures’ Exploration Problem with a complex input set is solved by evolving emergent agents. It shows that neither a sole increase in intelligence nor amount is the best solution. Instead, a cautious balance creates best results.