This strikes me as a rather weak idea from a genetic algorithms point of
view.
but you can create expressions by a recursive algorithm (given some chosen
maximum length) from the root
by picking an operation at each level, knowing the number of operands each
takes.
On Monday, July 25, 2016 at 6:22:31 AM UTC-7, Gasper Slapnicar wrote:
>
> Hello everyone.
>
> I am rather new to both symbolic regression and sympy. I am trying to use
> expressions (mathematical equations) to represent individuals in a genetic
> algorithm population. However, i came across the following issue:
>
> When trying to create an initial population, i have to create
> equations/expressions/trees from a fixed set of Symbols and operations. I
> do not know how to combine them into random combinations representing
> complete expression trees.
> Here is my code snippet:
>
> # Symbols
> x1, x2 = symbols('x1, x2')
> # Functions
> operations = [Add, Mul, Rational, Pow, sin, cos, tanh, log]
> for i in xrange(100):
> random_operation = randint(0, 7)
> print operations[random_operation]
>
>
> With this i manage to get a random operation as described here:
> http://docs.sympy.org/dev/tutorial/manipulation.html
>
> However, i would like to create complete examples of
> equations/expressions/trees, including my symbols, so that i can represent
> them as trees. My ultimate goal is to evolve this population by
> substituting certain operations/symbols for the new population (crossover,
> mutation, etc). For example, i would like to have (many different random
> combinations):
> >>> expr = (Symbol('x1')+Symbol('x2'))**2 / 3
> >>> srepr(expr)
> Mul(Rational(1,3),Pow(Add(Symbol(′x1′),Symbol(′x2′)),Integer(2)))
>
> Any help much appreciated.
>
> Thank you and kind regards,
> Gasper
>
>
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