Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
A Stream Processor Architecture Based on the Configurable CEPRA-S
FPL '00 Proceedings of the The Roadmap to Reconfigurable Computing, 10th International Workshop on Field-Programmable Logic and Applications
SOS++: finding smart behaviors using learning and evolution
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Implementation of the Massively Parallel Model GCA
PARELEC '04 Proceedings of the international conference on Parallel Computing in Electrical Engineering
Optimal behavior of a moving creature in the cellular automata model
PaCT'05 Proceedings of the 8th international conference on Parallel Computing Technologies
Emergent algorithms for centroid and orientation detection in high-performance embedded cameras
Proceedings of the 5th conference on Computing frontiers
Improving the Behavior of Creatures by Time-Shuffling
ACRI '08 Proceedings of the 8th international conference on Cellular Automata for Reseach and Industry
Evolving Multi-creature Systems for All-to-All Communication
ACRI '08 Proceedings of the 8th international conference on Cellular Automata for Reseach and Industry
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
Solving All-to-All Communication with CA Agents More Effectively with Flags
PaCT '09 Proceedings of the 10th International Conference on Parallel Computing Technologies
CA Models for Target Searching Agents
Electronic Notes in Theoretical Computer Science (ENTCS)
Solving the exploration's problem with several creatures more efficiently
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
All-to-all communication with CA agents by active coloring and acknowledging
ACRI'10 Proceedings of the 9th international conference on Cellular automata for research and industry
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
Are several creatures more efficient than a single one?
ACRI'06 Proceedings of the 7th international conference on Cellular Automata for Research and Industry
Effectively evolving finite state machines compared to enumeration
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
Concurrency and Computation: Practice & Experience
parallel hardware architecture to simulate movable creatures in the CA model
PaCT'07 Proceedings of the 9th international conference on Parallel Computing Technologies
Hi-index | 0.00 |
The goal of our investigation is to find automatically the absolutely best rule for a moving creature in a cellular field The task of the creature is to visit all empty cells with a minimum number of steps We call this problem creature's exploration problem The behaviour was modelled using a variable state machine represented by a state table Input to the state table is the current state and the neighbour's state in front of the creature's moving direction The problem is that the search space for the possible rules grows exponentially with the number of states, inputs and outputs We could solve the problem for six states, two inputs and two outputs with the aid of a parallel hardware platform (FPGA technology) The set of all possible n-state algorithms was first reduced by discarding equivalent, reducible and not strongly connected ones The algorithms which showed a certain performance for five initial configurations during simulation were extracted by the hardware and send to the host PC Additional tests for robustness and the behaviour of several creatures was carried out in software One creature with the best algorithm can visit 99.92 % of the empty cells of 26 test configurations Several creatures up to 16 can perform the task more efficiently for the tested initial configuration.