diff --git a/README.md b/README.md
index 805e42c..ffd3ab5 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,14 @@
 # GeneExpressionProgramming for symbolic regression
 The repository contains an implementation of the Gene Expression Programming [1], whereby the internal representation of the equation is fully tokenized as a vector of integers. This representation allows a lower memory footprint, leading to faster processing of the application of the genetic operators. Moreover, the implementation also contains a mechanism for semantic backpropagation, ensuring dimensional homogeneity for physical units [2]. 
 
+# Features
+- Standard GEP Symbolic Regression
+- Multi-Objective optimization
+- Population initialization based on Latin Hypercube Sampling
+- Coefficient Optimization
+- Matrix/ Tensor optimization
+- Phy. Dimensionality Consideration
+
 # How to use it?
 - Install the package:
   ```julia
@@ -166,7 +174,6 @@ print_karva_strings(lsg)
  - The Coefficient optimization is inspired by [https://github.com/MilesCranmer/SymbolicRegression.jl](https://github.com/MilesCranmer/SymbolicRegression.jl/blob/master/src/ConstantOptimization.jl)
  - We employ the insane fast [DynamicExpressions.jl](https://github.com/SymbolicML/DynamicExpressions.jl) for evaluating our expressions
 
-
 # How to cite
 Feel free to utilize it for your research, it would be nice __citing us__! Our [paper](https://doi.org/10.1007/s10710-025-09510-z).
 ```
diff --git a/src/Gep.jl b/src/Gep.jl
index 4052057..49bb53c 100644
--- a/src/Gep.jl
+++ b/src/Gep.jl
@@ -226,7 +226,7 @@ end
 """
 @inline function equation_characterization_default(population::Vector{Chromosome}, n_samples::Int; inputs_::Int=0)
     len_extented_pop = length(population)
-    coeff_count = isempty(population[1].toolbox.preamble_syms) ? 1 : length(length(population[1].toolbox.preamble_syms))
+    coeff_count = isempty(population[1].toolbox.preamble_syms) ? 1 : length(population[1].toolbox.preamble_syms)
     features = zeros(coeff_count * 2, len_extented_pop)
     prob_dataset = rand(Uniform(0, 1), 100, inputs_ == 0 ? 10 : inputs_)