402 research outputs found
Uniform coevolution for solving the density classification problem in cellular automata
Genetic and Evolutionary Computation Conference (GECCO 2000). Las Vegas, Nevada (USA), July 8-12 2000.Uniform Coevolution is based on competitive evolution ideas where the solution and example sets are evolving by means of a competition to generate difficult test beds for the solutions in a gradual way. The method has been tested with the density parity problem in cellular automata, where the selected examples can biased the solutions founded. The results show a high value of generality using Uniform coevolution, compared with no Co-evolutive approaches.Publicad
Resource Sharing and Coevolution in Evolving Cellular Automata
Evolving one-dimensional cellular automata (CAs) with genetic algorithms has
provided insight into how improved performance on a task requiring global
coordination emerges when only local interactions are possible. Two approaches
that can affect the search efficiency of the genetic algorithm are coevolution,
in which a population of problems---in our case, initial configurations of the
CA lattice---evolves along with the population of CAs; and resource sharing, in
which a greater proportion of a limited fitness resource is assigned to those
CAs which correctly solve problems that fewer other CAs in the population can
solve. Here we present evidence that, in contrast to what has been suggested
elsewhere, the improvements observed when both techniques are used together
depend largely on resource sharing alone.Comment: 8 pages, 1 figure; http://www.santafe.edu/~evca/rsc.ps.g
Coevolutive adaptation of fitness landscape for solving the testing problem
IEEE International Conference on Systems, Man, and Cybernetics. Nashville, TN, 8-11 October 2000A general framework, called Uniform Coevolution, is introduced to overcome the testing problem in evolutionary computation methods. This framework is based on competitive evolution ideas where the solution and example sets are evolving by means of a competition to generate difficult test beds for the solutions in a gradual way. The method has been tested with two different problems: the robot navigation problem and the density parity problem in cellular automata. In both test cases using evolutive methods, the examples used in the learning process biased the solutions found. The main characteristics of the Uniform Coevolution method are that it smoothes the fitness landscape and, that it obtains âideal learner examplesâ. Results using uniform coevolution show a high value of generality, compared with non co-evolutive approaches
Fitness landscape of the cellular automata majority problem: View from the Olympus
In this paper we study cellular automata (CAs) that perform the computational
Majority task. This task is a good example of what the phenomenon of emergence
in complex systems is. We take an interest in the reasons that make this
particular fitness landscape a difficult one. The first goal is to study the
landscape as such, and thus it is ideally independent from the actual
heuristics used to search the space. However, a second goal is to understand
the features a good search technique for this particular problem space should
possess. We statistically quantify in various ways the degree of difficulty of
searching this landscape. Due to neutrality, investigations based on sampling
techniques on the whole landscape are difficult to conduct. So, we go exploring
the landscape from the top. Although it has been proved that no CA can perform
the task perfectly, several efficient CAs for this task have been found.
Exploiting similarities between these CAs and symmetries in the landscape, we
define the Olympus landscape which is regarded as the ''heavenly home'' of the
best local optima known (blok). Then we measure several properties of this
subspace. Although it is easier to find relevant CAs in this subspace than in
the overall landscape, there are structural reasons that prevent a searcher
from finding overfitted CAs in the Olympus. Finally, we study dynamics and
performance of genetic algorithms on the Olympus in order to confirm our
analysis and to find efficient CAs for the Majority problem with low
computational cost
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