Convergence properties of incremental Bayesian evolutionary algorithms with single Markov chains

Proceedings of the IEEE Conference on Evolutionary Computation, ICEC

Volumn 2, Issue , 2000, Pages 938-945

  Zhang, Byoung Tak ^a   Paass, Gerhard ^a   Muehlenbein, Heinz ^a  

^a Seoul National University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN EVOLUTIONARY ALGORITHMS (BEA); EVOLUTIONARY COMPUTATION; MAXIMUM A POSTERIORI (MAP) ESTIMATION; ADAPTIVE MARKOV CHAIN MONTE CARLO; ASYMPTOTIC BEHAVIORS; CONVERGENCE PROPERTIES; DYNAMIC MONTE CARLO METHOD; MARKOV CHAIN ANALYSIS; MAXIMUM LIKELIHOOD ESTIMATE; PROBABILISTIC MODELING; REGULARITY CONDITION;

CONVERGENCE OF NUMERICAL METHODS; ITERATIVE METHODS; LEARNING ALGORITHMS; MARKOV PROCESSES; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; MONTE CARLO METHODS; OPTIMIZATION; PROBABILITY; SIMULATED ANNEALING; CALCULATIONS;

COMPUTATION THEORY; EVOLUTIONARY ALGORITHMS;

EID: 0033676664     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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