A rigorous analysis of the compact genetic algorithm for linear functions
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Publication:862957
DOI10.1007/s11047-006-9001-0zbMath1113.68100OpenAlexW1990799344MaRDI QIDQ862957
Publication date: 25 January 2007
Published in: Natural Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11047-006-9001-0
Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)
Related Items (15)
The complex parameter landscape of the compact genetic algorithm ⋮ Lower bounds on the run time of the univariate marginal distribution algorithm on OneMax ⋮ OneMax is not the easiest function for fitness improvements ⋮ Runtime analysis of the \((1+1)\) EA on computing unique input output sequences ⋮ How majority-vote crossover and estimation-of-distribution algorithms cope with fitness valleys ⋮ Upper bounds on the running time of the univariate marginal distribution algorithm on OneMax ⋮ Level-based analysis of the univariate marginal distribution algorithm ⋮ Optimizing linear functions with the \((1 + \lambda)\) evolutionary algorithm -- different asymptotic runtimes for different instances ⋮ On the choice of the update strength in estimation-of-distribution algorithms and ant colony optimization ⋮ The runtime of the compact genetic algorithm on jump functions ⋮ Runtime analyses of the population-based univariate estimation of distribution algorithms on LeadingOnes ⋮ Working principles of binary differential evolution ⋮ Runtime analysis of a binary particle swarm optimizer ⋮ The univariate marginal distribution algorithm copes well with deception and epistasis ⋮ On the model updating operators in univariate estimation of distribution algorithms
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