Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolutionary Algorithms in Engineering Applications
Evolutionary Algorithms in Engineering Applications
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Compositional evolution: interdisciplinary investigations in evolvability, modularity, and symbiosis
Compositional evolution: interdisciplinary investigations in evolvability, modularity, and symbiosis
Combining competent crossover and mutation operators: a probabilistic model building approach
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
On the complexity of hierarchical problem solving
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Overcoming hierarchical difficulty by hill-climbing the building block structure
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
Probabilistic model building methods can render difficult problems feasible by identifying and exploiting dependencies. They build a probabilistic model from the statistical properties of multiple samples (population) scattered in the search space and generate offspring according to this model. The memory requirements of these methods grow along with the problem size as the population must be large enough to guarantee proper initial-supply, decision-making and accurate model-building. The paper presents a novel model based trajectory method, which samples only one point at the time and infers the problem structure online by means of Artificial Neural Network based machine learning technique. As case study we show how the proposed method can very efficiently address hard, non-separable building-block problems, specially designed to be solvable only by population based recombinative methods. The small memory requirement and fast convergence of the proposed method comes at the cost of a tradeoff: the complexity of an accurate model building is bounded by the exponential of the order of dependencies detected by the online learning.