mbinois / algorithm-EGO

Funz algorithm: EGO

• Efficient Global Optimization (EGO)
• tags: optimization; sparse
• author: [e-mail]; DiceKriging authors
• require: DiceDesign; DiceKriging; DiceView; pso; jsonlite
• options: search_ymin='true'; initBatchSize='4'; batchSize='4'; iterations='10'; initBatchBounds='true'; trend='y~1'; covtype='matern3_2'; knots='0'; liar='upper95'; seed='1'
• options.help: search_ymin=minimization or maximisation; initBatchSize=Initial batch size; batchSize=iterations batch size; iterations=number of iterations; initBatchBounds=add input variables bounding values (2^d combinations); trend=(Universal) kriging trend; covtype=Kriging covariance kernel; knots=number of non-stationary points for each Xi; liar=liar value for in-batch loop (when batchsize>1); seed=random seed
• input: x=list(min=0,max=1)
• output: y=0.99

Funz algorithm: ECEGO

• Efficient Global Optimization (EGO) algorithm with equality constraints.
• tags: optimization; sparse; contraints
• author: [e-mail]; DiceKriging authors
• require: DiceDesign; DiceKriging; DiceView; pso; jsonlite
• options: search_ymin='true'; initBatchSize='4'; batchSize='4'; iterations='10'; initBatchBounds='true'; trend='y1'; covtype='matern3_2'; knots='0'; liar='upper95'; trend_constr='y1'; covtype_constr='matern3_2'; liar_constr='upper95'; seed='1'
• options.help: search_ymin=minimization or maximisation; initBatchSize=Initial batch size; batchSize=iterations batch size; iterations=number of iterations; initBatchBounds=add input variables bounding values (2^d combinations); trend=(Universal) kriging trend; covtype=Kriging covariance kernel; knots=number of non-stationary points for each Xi; liar=liar value for in-batch loop (when batchsize>1); seed=random seed
• input: x=list(min=0,max=1)
• output: y=0.99