Finite Markov chain analysis of genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms and fitness variance with an application to the automated design of artificial neural networks
Noise, sampling, and efficient genetic algorthms
Noise, sampling, and efficient genetic algorthms
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Variable Neighborhood Decomposition Search
Journal of Heuristics
Real royal road functions for constant population size
Theoretical Computer Science
Real royal road functions: where crossover provably is essential
Discrete Applied Mathematics - Special issue: Boolean and pseudo-boolean funtions
Where Genetic Algorithms Excel
Evolutionary Computation
Rigorous hitting times for binary mutations
Evolutionary Computation
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
Are multiple runs of genetic algorithms better than one?
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Substructural neighborhoods for local search in the bayesian optimization algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Fluctuating crosstalk as a source of deterministic noise and its effects on GA scalability
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Overcoming hierarchical difficulty by hill-climbing the building block structure
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Effects of discrete hill climbing on model building forestimation of distribution algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
On sample size control in sample average approximations for solving smooth stochastic programs
Computational Optimization and Applications
| Hi-index | 0.00 |
This paper analyzes the relative advantages between crossover and mutation on a class of deterministic and stochastic additively separable problems with substructures of non-uniform salience. This study assumes that the recombination and mutation operators have the knowledge of the building blocks (BBs) and effectively exchange or search among competing BBs. Facetwise models of convergence time and population sizing have been used to determine the scalability of each algorithm. The analysis shows that for deterministic exponentially-scaled additively separable, problems, the BB-wise mutation is more efficient than crossover yielding a speedup of o(llogl), where l is the problem size. For the noisy exponentially-scaled problems, the outcome depends on whether scaling on noise is dominant. When scaling dominates, mutation is more efficient than crossover yielding a speedup of o(llogl). On the other hand, when noise dominates, crossover is more efficient than mutation yielding a speedup of o(l).