diff --git a/include/Rivet/Tools/MendelMin.hh b/include/Rivet/Tools/MendelMin.hh
--- a/include/Rivet/Tools/MendelMin.hh
+++ b/include/Rivet/Tools/MendelMin.hh
@@ -1,213 +1,234 @@
 #ifndef RIVET_MendelMin_H
 #define RIVET_MendelMin_H
 
 #include "Rivet/Tools/Random.hh"
 #include <valarray>
 #include <random>
 #include <functional>
 #include <map>
 #include <vector>
 #include <cmath>
 #include <iostream>
 #include <iomanip>
 
 namespace Rivet {
 
 
   /// MendelMin implements a home brewed genetic algorithm for finding
   /// the minimum of a function defined on a unit hypercube returning a
   /// non-negative real number (eg. a Chi-squared value).
   class MendelMin {
   public:
 
     /// Typedef for a valaray of parameters to the function to be minimised.
     using Params = std::valarray<double>;
     /// Typedef for the function to be minimised
     using FuncT = std::function<double(const Params&, const Params&)>;
+    /// Typedef for the function to be minimised
+    using FuncNoFixedT = std::function<double(const Params&)>;
     // /// Typedef for the [0,1] random number generator
     // using RndT = std::function<double()>;
 
 
-    /// Constructor taking as arguments: the function, @a fin, to be
-    /// minimised; a random number generator, @rndin, returning double
-    /// random numbers between 0 and 1; the dimension, @a ndim, of the
-    /// unit hypercube for which @a fin is defined. Optional arguments
-    /// are: the number, @a npop, of individuals in the population; and
-    /// @a margin which determines how much ramndomness is involved when
-    /// an individual is evolved twowards the fittest individual.
-    MendelMin(const FuncT & fin, unsigned int ndim,
-              const Params & fixpar=Params(), //const RndT & rndin,
-              unsigned int npop = 20, unsigned int ngen = 20,
-              double margin = 0.1)
+    /// Constructor with fixed parameters
+    ///
+    /// Mandatory arguments: the function, @a fin, to be minimised;
+    /// the dimension, @a ndim, of the unit hypercube for which @a fin is
+    /// defined; a set of fixed parameters not to be optimised.
+    ///
+    /// Optional arguments are: the number, @a npop, of individuals in the
+    /// population; and @a margin which determines how much randomness is
+    /// involved when an individual is evolved twowards the fittest individual.
+    MendelMin(const FuncT& fin, unsigned int ndim,
+              const Params& fixpar, //const RndT & rndin,
+              unsigned int npop=20, unsigned int ngen=20,
+              double margin=0.1)
       : _f(fin), _q(fixpar), //_rnd(rndin),
         _NDim(ndim), _margin(margin),
         _pop(npop), _fit(npop, -1.0), showTrace(false) {}
 
 
+    /// Constructor without fixed parameters
+    ///
+    /// Mandatory arguments: the function, @a fin, to be minimised; the
+    /// dimension, @a ndim, of the unit hypercube for which @a fin is defined.
+    ///
+    /// Optional arguments are: the number, @a npop, of individuals in the
+    /// population; and @a margin which determines how much randomness is
+    /// involved when an individual is evolved twowards the fittest individual.
+    MendelMin(const FuncNoFixedT& fin, unsigned int ndim,
+              //const RndT & rndin,
+              unsigned int npop=20, unsigned int ngen=20,
+              double margin=0.1)
+      : MendelMin([&](const Params& ps, const Params&) -> double { return fin(ps); },
+                  ndim, {}, npop, ngen, margin)
+    {   }
+
+
     /// Supply a best guess for the fittest parameter point to help
     /// things along.
     void guess(const Params & p) {
       _pop.push_back(p);
       limit01(_pop.back());
       _fit.push_back(-1.0);
     }
 
 
     /// Evolve the population a given number of generations and return
     /// the best fit value.
     double evolve(unsigned int NGen) {
       for ( unsigned n = 0; n < NGen; ++n ) {
         // Calculate the fitness.
         auto mm = minmax();
         // Always kill the fittest individual.
         if ( showTrace ) _debug();
         for ( unsigned int i = 1; i < _pop.size(); ++i ) {
           if ( _fit[i] > rnd()*(mm.second - mm.first) )
             // Kill all individuals that have low fitness or are just unlucky.
             _fit[i] = -1.0;
           else
             // Improve This individual to be more like the fittest.
             move(_pop[i],_pop[0]);
         }
       }
       return _fit[0];
     }
 
     /// Return the fittest parameter point found.
     Params fittest() const {
       return _pop[0];
     }
 
     /// Return the fittest value found.
     double fit() const {
       return _fit[0];
     }
 
     /// Simple wrapper around the random number generator.
     double rnd() const {
       return rand01(); //_rnd();
     }
 
     /// Return a random parameter point in the unit hypercube.
     Params rndParams() const {
       Params ret(_NDim);
       for ( unsigned int i = 0; i < _NDim; ++i ) ret[i] = rnd();
       return ret;
     }
 
     /// Limit a parameter point to inside the unit hypercube.
     void limit01(Params & p) const {
       for ( unsigned int i = 0; i < _NDim; ++i )
         p[i] = std::max(0.0, std::min(p[i], 1.0));
     }
 
     /// Move an @a bad parameter point towards a @a better one. The new
     /// point is picked randomly within the generalized hypercube where
     /// @bad and @better are at diagonally opposite corners, enlarged by
     /// a fraction _margin.
     void move(Params & bad, const Params & better) const {
       bad += (better - bad)*(rndParams()*(1.0 + 2.0*_margin) - _margin);
       limit01(bad);
     }
 
     /// Simple wrapper around the function to be minimised.
     double f(const Params & p) const {
       return _f(p, _q);
     }
 
 
     /// Calculate the fitness values of all individuals and put the
     /// fittest one first. @return the best and worst fitness values.
     std::pair<double, double> minmax() {
       std::pair<double,double> mm(std::numeric_limits<double>::max(), 0.0);
       unsigned int iwin = 0;
       for ( unsigned int i = 0; i < _pop.size(); ++i ) {
         double & v = _fit[i];
         // negative fitness value means the individual is dead, so we
         // welocme a new immigrant.
         if ( v < 0.0 ) _pop[i] = rndParams();
 
         // The calculated fitness value cannot be negative.
         v = std::max(0.0, f(_pop[i]));
 
         // Compare to the best and worst fitness so far.
         if ( v < mm.first ) iwin = i;
         mm.first = std::min(v, mm.first);
         mm.second = std::max(mm.second, v);
       }
 
       // Move the winner to the top.
       if ( iwin != 0 ) {
         std::swap(_pop[0], _pop[iwin]);
         std::swap(_fit[0], _fit[iwin]);
       }
       return mm;
     }
 
     /// Inspect a population and the fitness of its individuals.
     void _debug() {
       std::cout << "GenAlgMax population status:" << std::endl;
       for ( unsigned int i = 0; i < _pop.size(); ++i ) {
         std::cout << std::setw(10) << _fit[i] << " (" << _pop[i][0];
         for ( unsigned int ip = 1; ip < _NDim; ++ip )
           std::cout << "," << _pop[i][ip];
         std::cout << ")" << std::endl;
       }
     }
 
 
   private:
 
     /// The function to be minimised.
     const FuncT _f;
 
     /// The fixed parameter points.
     Params _q;
 
     /// The fixed parameters of the function.
     //  const double _q;
 
     /// The random number generator.
     //const RndT _rnd;
 
     /// The dimension of the unit hypercube of allowed parameter points.
     unsigned int _NDim;
 
     /// WHen evolving less fit parameter point towards the fittest one
     /// we choose randomly in a generalised hypercube where these two
     /// parameter points are in diagonally opposite corners, expanded in
     /// each direction with This fraction.
     double _margin;
 
     /// The population of parameter points.
     std::vector<Params> _pop;
 
 
     /// The fitness value of each individual in the population.
     std::vector<double> _fit;
 
   public:
 
     /// Set true to get a verbose record of the evolution.
     bool showTrace;
 
   };
 
 
   /// Construct a MendelMin object taking as arguments: the function, @a
   /// fin, to be minimised; a random number generator, @rndin, returning
   /// double random numbers between 0 and 1; the dimension, @a ndim, of
   /// the unit hypercube for which @a fin is defined; the number, @a
   /// npop, of individuals in the population; and a @a margin which
   /// determines how much ramndomness is involved when an individual is
   /// evolved twowards the fittest individual.
   // template <typename FuncT, typename RndT>
   // MendelMin <FuncT, RndT>
   // makeMendelMin(const FuncT & f, const RndT & rnd, unsigned int ndim,
   //               unsigned int npop = 20, double margin = 0.1) {
   //   return MendelMin<FuncT, RndT>(f, rnd, ndim, npop, margin);
   // }
 
 }
 
 #endif // RIVET_MendelMin_H