On the effects of node duplication and connection-oriented constructivism in neural XCSF
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Wireless heterogeneous transmitter placement using multiobjective variable-length genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
New crossover operators in linear genetic programming for multiclass object classification
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolving spiking networks with variable memristors
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Evolving spiking networks with variable memristors
ACM SIGEVOlution
Meaningful representation and recombination of variable length genomes
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Hi-index | 0.00 |
The synapsing variable-length crossover (SVLC) algorithm provides a biologically inspired method for performing meaningful crossover between variable-length genomes. In addition to providing a rationale for variable-length crossover, it also provides a genotypic similarity metric for variable-length genomes, enabling standard niche formation techniques to be used with variable-length genomes. Unlike other variable-length crossover techniques which consider genomes to be rigid inflexible arrays and where some or all of the crossover points are randomly selected, the SVLC algorithm considers genomes to be flexible and chooses nonrandom crossover points based on the common parental sequence similarity. The SVLC algorithm recurrently "glues" or synapses homogenous genetic subsequences together. This is done in such a way that common parental sequences are automatically preserved in the offspring with only the genetic differences being exchanged or removed, independent of the length of such differences. In a variable-length test problem, the SVLC algorithm compares favorably with current variable-length crossover techniques. The variable-length approach is further advocated by demonstrating how a variable-length genetic algorithm (GA) can obtain a high fitness solution in fewer iterations than a traditional fixed-length GA in a two-dimensional vector approximation task