Search

CADET-Match uses a number of different search strategies for parameter estimation. Parameters that are common to more than one search method will be repeated. The choice of search strategy is set by using searchMethod.

Key	Values	Default	Required	Description
searchMethod	String	UNSGA3	No	Select the search method to use. (UNSGA3, NSGA3, Multistart, GraphSpace, MCMC, AltScore, Gradient, ScoreTest)

Most of the search strategies are population based and capable of running in parallel.

UNSGA3

UNSGA3 is a genetic algorithm designed for high-dimensional problems. UNSGA3 from pymoo is used.

The difference between UNSGA3 and NSGA3 is that UNSGA3 uses a tournament selection to bread the best members instead of random mating. In almost all cases UNSGA3 will converge faster and to a better optimum. NSGA3 is mostly retained for backwards compatibility.

Key	Values	Default	Required	Description
population	Integer	100	No	Set the population size per parameter estimated.
generations	Integer	1000	No	Set the max number of generations to population size * generations.
sobolGeneration	Boolean	True	No	Use a sobol sequence to generate the initial population instead of random initialization
stallGenerations	Integer	10	No	Terminate optimization after this many generations without progress
stopAverage	Float	0.0	No	Stop searching when the average value of all metrics is less than or equal to this value
stopBest	Float	0.0	No	Stop searching when the highest metric is less than or equal to this value
stopRMSE	Float	0.0	No	Stop when the Root Mean Square Error is less than or equal to this value
gradCheck	Float	0.0	No	If the geometric mean of the metrics drop below this value run gradient descent to refine the value
finalGradRefinement	Boolean	False	No	Take the final pareto front and refine it with gradient descent
gradFineStop	Float	1e-14	No	set xtol of scipy.optimize.least_squares
localRefine	String	gradient	No	Can be gradient or powell
continueMCMC	Boolean	False	No	Once search finishes start MCMC and carry over useful information automatically
seeds	List	None	No	Optimization can be seeded with specific values to test
MultiObjectiveSSE	Boolean	False	No	If set to true SSE objectives are split apart and turned into a multi-objective system. This is useful for multiple experiments.

NSGA3

NSGA3 is a genetic algorithm designed for high-dimensional problems. NSGA3 from pymoo is used. In most cases UNSGA3 should be used and NSGA3 is retained for backwards compatibility.

Key	Values	Default	Required	Description
population	Integer	100	No	Set the population size per parameter estimated.
generations	Integer	1000	No	Set the max number of generations to population size * generations.
sobolGeneration	Boolean	True	No	Use a sobol sequence to generate the initial population instead of random initialization
stallGenerations	Integer	10	No	Terminate optimization after this many generations without progress
stopAverage	Float	0.0	No	Stop searching when the average value of all metrics is less than or equal to this value
stopBest	Float	0.0	No	Stop searching when the highest metric is less than or equal to this value
stopRMSE	Float	0.0	No	Stop when the Root Mean Square Error is less than or equal to this value
gradCheck	Float	0.0	No	If the geometric mean of the metrics drop below this value run gradient descent to refine the value
finalGradRefinement	Boolean	False	No	Take the final pareto front and refine it with gradient descent
gradFineStop	Float	1e-14	No	set xtol of scipy.optimize.least_squares
localRefine	String	gradient	No	Can be gradient or powell
continueMCMC	Boolean	False	No	Once search finishes start MCMC and carry over useful information automatically
seeds	List	None	No	Optimization can be seeded with specific values to test
MultiObjectiveSSE	Boolean	False	No	If set to true SSE objectives are split apart and turned into a multi-objective system. This is useful for multiple experiments.

Multistart

This is a simple MultiStart gradient descent search. If your problem is known to be solveable with gradient descent this can be a good search choice. While this search algorithm can be used as a precursor for MCMC that is not recomended.

Key	Values	Default	Required	Description
population	Integer	100	No	Set the population size per parameter estimated.
sobolGeneration	Boolean	True	No	Use a sobol sequence to generate the initial population instead of random initialization
gradFineStop	Float	1e-14	No	set xtol of scipy.optimize.least_squares
localRefine	String	gradient	No	Can be gradient or powell
continueMCMC	Boolean	False	No	Once search finishes start MCMC and carry over useful information automatically
seeds	List	None	No	Optimization can be seeded with specific values to test

GraphSpace

The purpose of GraphSpace is to create a map of your problem. It is designed to be used with an extremely large population and it will just evaluate the goal, generate graphs and terminate. It will not actually optimize. What it does tell you is what variable ranges are reasonable to optimize in. This is a good first step for any new problem where the ranges of the parameters is extremely large and the viable ranges are unknown.

Key	Values	Default	Required	Description
population	Integer	100	No	Set the population size per parameter estimated.
sobolGeneration	Boolean	True	No	Use a sobol sequence to generate the initial population instead of random initialization
gradFineStop	Float	1e-14	No	set xtol of scipy.optimize.least_squares
localRefine	String	gradient	No	Can be gradient or powell
continueMCMC	Boolean	False	No	Once search finishes start MCMC and carry over useful information automatically
seeds	List	None	No	Optimization can be seeded with specific values to test

MCMC

MCMC is used for error modeling. It is highly suggested that NSGA3 is used with continueMCMC. MCMC is not an optimization algorithm and should not be used as one. In order to run an error model is also required.

Key	Values	Default	Required	Description
MCMCpopulation	Integer	population	No	Set the population size per parameter estimated.
MCMCpopulationSet	Integer	None	No	Set the total population size (overrides MCMCpopulation)
mcmc_h5	Path	None	No	This is the path to the mcmc.h5 file generated in a previous MCMC run to use as a prior.
PreviousResults	Path	None	No*	MCMC is not an optimization algorithm and should not be directly run. Use NSGA3 and continueMCMC True and this will be auto set.
MCMCTauMult	Integer	50	No	MCMC runs until the chain length/integrated autocorrelation time < MCMCTauMult+2 (2 for burn in)

AltScore

AltScore is very rarely used. What it allows is reading another completed result and will just re-evaluate the entries of the pareto front with a different goal and report the results. This can be useful for goal design to see the impact of combining different scores and if that would make the problem easier or harder to optimize.

Key	Values	Default	Required	Description
PreviousResults	Path	None	Yes	Look at the previous results and reevaluate the pareto front with a different goal (useful to see the impact of different scores)

Gradient

This is a simple test search algorithm that reads seeds and runs gradient descent from them. This search algorithm has little practical usage and requires good starting points.

Key	Values	Default	Required	Description
gradFineStop	Float	1e-14	No	set xtol of scipy.optimize.least_squares
localRefine	String	gradient	No	Can be gradient or powell
seeds	List	None	Yes	Optimization can be seeded with specific values to test

ScoreTest

This is the simplest of all the systems and just designed for testing goals. Given one or more seeds they are simulated, scored and results returned.

Key	Values	Default	Required	Description
seeds	List	None	Yes	Optimization can be seeded with specific values to test