Name
Abstract | ||
|---|---|---|
We present a new method for using genetic algorithms and L systems to grow up efficient neural network structures. Our L rules operate directly on 2-dimensional cell matrix, L, rules are produced automatically by genetic algorithm and they have "age" that controls the number of firing times, i.e., times we can apply each rule. We have modified the conventional neural network model so that it is easy to present the knowledge by birth (axon weights) and the learning by experience (dendrite weights). A connection is shown to exist between the axon weights and learning parameters used e.g., in back propagation. This system enables us to find special structures that are very fast for both to train and to operate comparing to conventional, layered methods. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1080/00207169908804880 | INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS |
Keywords | Field | DocType |
genetic algorithms, Lindenmayer systems, back propagation, network structure, xor problem | Computer science,Algorithm,Artificial intelligence,Xor problem,Artificial neural network,Backpropagation,Genetic algorithm,Network structure | Journal |
Volume | Issue | ISSN |
73 | 1 | 0020-7160 |
Citations | PageRank | References |
3 | 0.39 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Isto Aho | 1 | 16 | 2.83 |
| Harri Kemppainen | 2 | 9 | 1.23 |
| Kai Koskimies | 3 | 708 | 92.29 |
| erkki makinen | 4 | 3 | 0.39 |
| Tapio Niemi | 5 | 163 | 18.90 |