Quoting Wikipedia

"In artificial intelligence, an Evolutionary Algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection."

"Multiobjective optimization is an area of multiple criteria decision making, that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously."

Whenever the number of objectives exceeds three, the problems suffer from Curse of dimensionality.

So, what I have done is given a larger number of objectives, find the subset of the necessary objectives required to represent the dimension of true pareto front. After reducing objectives, existing evolutionary algorithms perform better as compared to original set of objectives.