A Mathematical Analysis of Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Convergence Models of Genetic Algorithm Selection Schemes
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
On the Mean Convergence Time of Evolutionary Algorithms without Selection and Mutation
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
A markov chain framework for the simple genetic algorithm
Evolutionary Computation
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
A test problem with adjustable degrees of overlap and conflict among subproblems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Linkage learning by number of function evaluations estimation: Practical view of building blocks
Information Sciences: an International Journal
Design of test problems for discrete estimation of distribution algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
This paper aims at detecting the existence of building blocks directly from the fitness function without performing genetic algorithms. To do so, this paper extends the convergence time model and the gambler's ruin model so they can be applied to a larger variety of problems. With proposed models, the number of fitness evaluations can be estimated for both of these two cases: (1) some genes are transferred together in crossover (treated as a building block); (2) the genes are transferred separately. Therefore, we can compare the number of fitness evaluations and detect the existence of building blocks for a large family of fitness functions without actually performing a genetic algorithm.