A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Finite Markov chain analysis of genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Generalized Convergence Models for Tournament- and (mu, lambda)-Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Genetic algorithms as function optimizers
Genetic algorithms as function optimizers
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
Empirical analysis of a genetic algorithm-based stress test technique
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Enhancing Efficiency of Hierarchical BOA Via Distance-Based Model Restrictions
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Analyzing probabilistic models in hierarchical BOA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
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This paper analyzes the behavior of a selectorecombinative genetic algorithm (GA) with an ideal crossover on a class of random additively decomposable problems (rADPs). Specifically, additively decomposable problems of order k whose subsolution fitnesses are sampled from the standard uniform distribution U[0,1] are analyzed. The scalability of the selectorecombinative GA is investigated for 10,000 rADP instances. The validity of facetwise models in bounding the population size, run duration, and the number of function evaluations required to successfully solve the problems is also verified. Finally, rADP instances that are easiest and most difficult are also investigated.