The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Addressing sampling errors and diversity loss in UMDA
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Genetic algorithms, selection schemes, and the varying effects of noise
Evolutionary Computation
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
A memory efficient and continuous-valued compact EDA for large scale problems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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This paper analyzes the effects of an initial-population bias on the performance of the univariate marginal distribution algorithm (UMDA). The analysis considers two test problems: (1) onemax and (2) noisy onemax. Theoretical models are provided and verified with experiments. Intuitively, biasing the initial population toward the global optimum should improve performance of UMDA, whereas biasing the initial population away from the global optimum should have the opposite effect. Both theoretical and experimental results confirm this intuition. Effects of mutation on performance of UMDA with initial-population bias are also investigated.