Generalizing the notion of schema in genetic algorithms
Artificial Intelligence
Isomorphisms of genetic algorithms
Artificial Intelligence
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A New Class of the Crossover Operators for the Numerical Optimization
Proceedings of the 6th International Conference on Genetic Algorithms
An Adaptive Poly-Parental Recombination Strategy
Selected Papers from AISB Workshop on Evolutionary Computing
A comparison of the fixed and floating building block representation in the genetic algorithm
Evolutionary Computation
Forking genetic algorithms: Gas with search space division schemes
Evolutionary Computation
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Automatic fuzzy rules generation using fuzzy genetic algorithm
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 6
| Hi-index | 0.00 |
In genetic search algorithms and optimization routines, the representation of the mutation and crossover operators are typically defaulted to the canonical basis. We show that this can be influential in the usefulness of the search algorithm. We then pose the question of how to find a basis for which the search algorithm is most useful. The conjugate schema is introduced as a general mathematical construct and is shown to separate a function into smaller dimensional functions whose sum is the original function. It is shown that conjugate schema, when used on a test suite of functions, improves the performance of the search algorithm on 10 out of 12 of these functions. Finally, a rigorous but abbreviated mathematical derivation is given in the appendices.