At each iteration, this tries a random vector from the unit hypercube. (Your target function should scale the candidate vectors as necessary to cover the desired space.)
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| | GRandomSearch (GTargetFunction *pCritic, GRand *pRand) |
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| virtual | ~GRandomSearch () |
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| virtual double * | currentVector () |
| | Returns the best vector yet found. More...
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| virtual double | iterate () |
| | Try another random vector. More...
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| | GOptimizer (GTargetFunction *pCritic) |
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| virtual | ~GOptimizer () |
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| double | searchUntil (size_t nBurnInIterations, size_t nIterations, double dImprovement) |
| | This will first call iterate() nBurnInIterations times, then it will repeatedly call iterate() in blocks of nIterations times. If the error heuristic has not improved by the specified ratio after a block of iterations, it will stop. (For example, if the error before the block of iterations was 50, and the error after is 49, then training will stop if dImprovement is > 0.02.) If the error heuristic is not stable, then the value of nIterations should be large. More...
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At each iteration, this tries a random vector from the unit hypercube. (Your target function should scale the candidate vectors as necessary to cover the desired space.)
Public Member Functions inherited from GClasses::GOptimizer