top #### Explicit Equations **Explicit equations** are drawn from the SR literature. The main source is a paper titled ["GP Needs Better Benchmarks."](https://cs.gmu.edu/~sean/papers/gecco12benchma3ks4.pdf) We select a subset of these problems, those that have been used more often in published work. In several cases we added parameters to the equations for two reasons. The first is to bring the scale of the data into a reasonable range. We did this so that we could add percentage noise based on the variance of the data. The second reason was to make the equation more complex, thus making the regression tasks more difficult. ##### **Single Variable** {% for item in site.data.sr.benchmarks.explicit_1var %}
{{item.name}}
$$ {{item.latex}} $$
{% endfor %} ##### **Two variable** {% for item in site.data.sr.benchmarks.explicit_2var %}
{{item.name}}
$$ {{item.latex}} $$
{% endfor %} ##### **Five variable** {% for item in site.data.sr.benchmarks.explicit_5var %}
{{item.name}}
$$ {{item.latex}} $$
{% endfor %}
top #### NIST Regressions **NIST problems** are taken from the NIST website. These benchmarks are used to test linear and non-linear regression techniques. Here we apply PGE to these same problems. [NIST website](http://www.itl.nist.gov/div898/strd/general/dataarchive.html) ##### Linear Problems {% for item in site.data.sr.benchmarks.nist-linear %}
{{item.name}}
$$ {{item.latex}} $$
{{item.source}}
{% endfor %} ##### Noninear Problems {% for item in site.data.sr.benchmarks.nist-nonlinear %}
{{item.name}}
$$ {{item.latex}} $$
{{item.source}}
{% endfor %}
top #### Differential Equations For each of these, additionally add 1. description, external link 2. parameters / constants {% for item in site.data.sr.benchmarks.diffeqs %}
{{item.name}}
{% for eqn in item.latex %}
$$ {{eqn}} $$
{% endfor %}
{% endfor %}
top #### Additional Datasets ##### Yeast ##### Real World For each of these, additionally add 1. description, external link 2. will not have equations upfront **SymbolicRegression.com benchmarks** **Lake Data** ">next   index

Appendix B - Comparing Implementations




top #### Explicit Equations **Explicit equations** are drawn from the SR literature. The main source is a paper titled ["GP Needs Better Benchmarks."](https://cs.gmu.edu/~sean/papers/gecco12benchma3ks4.pdf) We select a subset of these problems, those that have been used more often in published work. In several cases we added parameters to the equations for two reasons. The first is to bring the scale of the data into a reasonable range. We did this so that we could add percentage noise based on the variance of the data. The second reason was to make the equation more complex, thus making the regression tasks more difficult. ##### **Single Variable** {% for item in site.data.sr.benchmarks.explicit_1var %}
{{item.name}}
$$ {{item.latex}} $$
{% endfor %} ##### **Two variable** {% for item in site.data.sr.benchmarks.explicit_2var %}
{{item.name}}
$$ {{item.latex}} $$
{% endfor %} ##### **Five variable** {% for item in site.data.sr.benchmarks.explicit_5var %}
{{item.name}}
$$ {{item.latex}} $$
{% endfor %}
top #### NIST Regressions **NIST problems** are taken from the NIST website. These benchmarks are used to test linear and non-linear regression techniques. Here we apply PGE to these same problems. [NIST website](http://www.itl.nist.gov/div898/strd/general/dataarchive.html) ##### Linear Problems {% for item in site.data.sr.benchmarks.nist-linear %}
{{item.name}}
$$ {{item.latex}} $$
{{item.source}}
{% endfor %} ##### Noninear Problems {% for item in site.data.sr.benchmarks.nist-nonlinear %}
{{item.name}}
$$ {{item.latex}} $$
{{item.source}}
{% endfor %}
top #### Differential Equations For each of these, additionally add 1. description, external link 2. parameters / constants {% for item in site.data.sr.benchmarks.diffeqs %}
{{item.name}}
{% for eqn in item.latex %}
$$ {{eqn}} $$
{% endfor %}
{% endfor %}
top #### Additional Datasets ##### Yeast ##### Real World For each of these, additionally add 1. description, external link 2. will not have equations upfront **SymbolicRegression.com benchmarks** **Lake Data** ">next ()