Handling eqaulity constraints in genetic algorithms

I am trying to solve an optimization problem with strength pareto algorithm (SPEA2). My decision variable have lower and upper bounds as well as an equality constraint (sum(dp) = 1). I am unable to handle equality constraints. Please advise.

• Can you give a self-contained description of the optimization problem? What objective function are you trying to maximize? Are there any other constraints? Are there any constraints that relate $\phi_j$ to $p_j$? – D.W. Jul 10 '17 at 19:16
• For this specific case, just have your algorithm directly model $p_1$ through $p_{ n-1}$, and set $p_n$ equal to 1 minus their sum. That way, you're guaranteed to meet the equality constraint. – deong Jul 10 '17 at 21:32