Convergence Theory and Applications of the Factorized Distribution Algorithm

摘要: The paper investigates the optimization of additively decomposable functions (ADF) by a new evolutionary algorithm called Factorized Distribution Algorithm (FDA). FDA is based on factorization distribution to generate search points. First separable ADFs are considered. These mapped generalized linear with metavariables defined for multiple alleles. mapping transforms into an Univariate Marginal Frequency (UMDA). For UMDA exact equation response selection is.computed under assumption proportionate selection. truncation approximate time convergence used, derived from analysis OneMax function. also numerically investigated non functions. very similar ADFs. outpe1iorms genetic recombination strings far.