The evolution of evolvability in genetic programming
Advances in genetic programming
Accurate Replication in Genetic Programming
Proceedings of the 6th International Conference on Genetic Algorithms
Optimized Nearest-Neighbor Classifiers Using Generated Instances
KI '96 Proceedings of the 20th Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Recurrent genetic algorithms: sustaining evolvability
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
| Hi-index | 0.00 |
Bloat is a common problem with Evolutionary Algorithms (EAs) that use variable length representation. By creating unnecessarily large individuals it results in longer EA runtimes and solutions that are difficult to interpret. The causes of bloat are still uncertain, but one theory suggests that it occurs when the phenotype (e.g. behaviors) of the parents are not successfully inherited by their offspring. Noting the similarity to evolvability theory, which measures heritability of fitness, we hypothesize that reproductive operators with high evolvability will be less likely to cause bloat. We set out to design a new crossover operator for Pittsburgh approach classifier systems that has high phenotypic heritability. We saw an opportunity using the nearest neighbor representation to perform crossover cuts in phenotype space rather than on the genomes. We demonstrate that our operator tends to be less susceptible to bloat and has higher evolvability than a standard Pittsburgh approach crossover operator. Our hope is that this will lead to a general approach to reducing bloat for any representation.