Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
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
Marching-pixels: a new organic computing paradigm for smart sensor processor arrays
Proceedings of the 2nd conference on Computing frontiers
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
Are several creatures more efficient than a single one?
ACRI'06 Proceedings of the 7th international conference on Cellular Automata for Research and Industry
Optimal behavior of a moving creature in the cellular automata model
PaCT'05 Proceedings of the 8th international conference on Parallel Computing Technologies
Improving the Behavior of Creatures by Time-Shuffling
ACRI '08 Proceedings of the 8th international conference on Cellular Automata for Reseach and Industry
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We are presenting results of the creature's exploration problem with several creatures. The task of the creatures is to visit all empty cells in an environment with obstacles in shortest time and with a maximum of efficiency. The cells are arranged in a regular 2D grid and the underlying processing model is a Cellular Automaton (CA). We have investigated the question how many creatures and which algorithm should be used in order to fulfill the task most efficiently with lowest cost. We use a set of 10 different behaviors (algorithms) for the creature which have proved to be very efficient in the case where only one creature explores the environment. These algorithms were found by exhaustive search and evaluation by the aid of hardware (FPGA) implementation. Different environments and a varying number (1 to 64) of creatures were used in simulations in order to evaluate the cooperative work and efficiency. It turned out that for each environment a certain number of creatures and a certain algorithm is cost optimal in terms of work units. The total amount of work using one creature with the best algorithm X is many cases higher than the work using n creature with an adequate algorithm Y. Using several creatures, positive conflicts arise which may help to solve the problem more efficiently.