Changeset 1254 for cpp/frams/genetics/genooperators.h

Timestamp:

06/22/23 03:29:05 (15 months ago)

Author:

Maciej Komosinski

Message:

• turn -0.0 into 0.0 if we have minimum==0.0, so that it does not look as if we got a value below 0.0 (minimum) after mutation (even though -0.0==0.0)
• implemented stochastic rounding for the mutation of integer parameters

File:

• cpp/frams/genetics/genooperators.h (modified) (2 diffs)

cpp/frams/genetics/genooperators.h

@@ -195 +195 @@
         static double mutateCreep(char type, double current, double mn, double mx, double stddev, bool limit_precision_3digits); ///<just as mutateCreepNoLimit(), but forces mutated value into the [mn,mx] range using the 'reflect' approach.
         static double mutateCreep(char type, double current, double mn, double mx, bool limit_precision_3digits); ///<just as mutateCreepNoLimit(), but forces mutated value into the [\a mn,\a mx] range using the 'reflect' approach and assumes standard deviation to be a fraction of the mx-mn interval width.
-        static void setIntFromDoubleWithProbabilisticDithering(ParamInterface &p, int index, double value); ///<sets a double value in an integer field; when a value is non-integer, applies random "dithering" so that both lower and higher integer value have some chance to be set.
+        static void setIntFromDoubleWithProbabilisticDithering(ParamInterface &p, int index, double value); ///<sets a double value in an integer field; when a value is non-integer, applies stochastic rounding (random "dithering") so that both lower and higher integer value have some chance to be set.
         static void linearMix(vector<double> &p1, vector<double> &p2, double proportion); ///<mixes two real-valued vectors; inherited proportion should be within [0,1]; 1.0 does not change values (all inherited), 0.5 causes both vectors to become their average, 0.0 swaps values (none inherited).
         static void linearMix(ParamInterface &p1, int i1, ParamInterface &p2, int i2, double proportion); ///<mixes i1'th and i2'th properties of p1 and p2; inherited proportion should be within [0,1]; 1.0 does not change values (all inherited), 0.5 causes both properties to become their average, 0.0 swaps values (none inherited). For integer properties applies random "dithering" when necessary.
@@ -221 +221 @@
         static string simplifiedModifiersFixedOrder(const char *str_of_char_pairs, vector<int> &char_counts); ///<returns a sequence of chars from \a str_of_char_pairs based on how many times each char occurred in \a char_counts. Assume that an even-index char and the following odd-index char have the opposite influence, so they cancel out. We don't use this function, because a fixed order imposed by this function means that the number of different parameter values produced by a sequence of modifiers is lowered (N same-letter upper- and lower-case chars yield only 2*N different values). Due to how modifiers work, the effect of aaA, aAa, Aaa etc. is different (N same-letter upper- and lower-case chars yield 2^N different values), so simplifying modifiers should not impose any order, should not interfere with their original order, and should not cancel out antagonistic modifiers - see \a simplifiedModifiers() and geneprops_test.cpp.
         //@}
-        static string simplifiedModifiers(const string &original); ///<from the \a original sequence removes modifiers that are too numerous (exceeding a defined threshold number), starting the removal from the leftmost (="oldest" when interpreting the sequence from left to right) ones.
+        static string simplifiedModifiers(const string &original); ///<from the \a original sequence removes modifiers that are too numerous (exceeding a defined threshold number), starting the removal from the least-significant, leftmost (="oldest" when interpreting the sequence from left to right) ones. Contrary to \a simplifiedModifiersFixedOrder(), this kind of simplification preserves 2^N different sequences for each upper/lower-case modifier and thus 2^N different values of a given property (see geneprops.cpp), but the values resulting from these sequences constitute a landscape not as easy for optimization as in the case of 2*N, where the effect of each mutation could be independent and additive (no epistasis). So for a given sequence length, the 2^N case allows for a higher resolution at the cost of a more rugged fitness landscape than the 2*N case.
 };