A generalized stationary point convergence theory for evolutionary algorithms

Conference · Sat Feb 01 00:00:00 EST 1997

OSTI ID:486145

Hart, W E ^[1]

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1. Sandia National Labs., Albuquerque, NM (United States). Applied and Numerical Mathematics Dept.

This paper presents a convergence theory for evolutionary pattern search algorithms (EPSAs). EPSAs are self-adapting evolutionary algorithms that modify the step size of the mutation operator in response to the success of previous optimization steps. Previously, the authors have proven a stationary point convergence theory for EPSAs for which the step size is not allowed to increase. The present analysis generalizes this analysis to prove a convergence theory for EPSAs that are allowed to both increase and decrease the step size. This convergence theory is based on an extension of the convergence theory for generalized pattern search methods.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE Office of Financial Management and Controller, Washington, DC (United States)
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 486145
• Report Number(s): SAND-97-0560C; CONF-970740-2; ON: DE97004388; TRN: AHC29713%%92
• Resource Relation: Conference: 7. international conference on genetic algorithms, Lansing, MI (United States), 19 Jul 1997; Other Information: PBD: Feb 1997
• Country of Publication: United States
• Language: English

Related Subjects

99 MATHEMATICS
COMPUTERS
INFORMATION SCIENCE
MANAGEMENT
LAW
MISCELLANEOUS
CONVERGENCE
ALGORITHMS
ITERATIVE METHODS
MATHEMATICAL OPERATORS
FUNCTIONS
OPTIMIZATION
STOCHASTIC PROCESSES