Multiobjective Evolutionary Algorithms (MOEA) are one of the hottest topics in the
area of evolutionary computation.  A multiobjective optimisation problem (MOP) may
have many, or even infinite Pareto optimal solutions. MOEAs aim at finding a number
of well-representative Pareto solutions for a decision maker.  Most current MOEAs do
not take advantage of the results in traditional mathematical programming.

MOEA/D and RM-MEDA are two very recent MOEAs, developed at Essex, which uses ideas
from traditional optimisation methods. In this talk, I will explain the motivations,
ideas, and main steps of these two methods, and show you some experimental results.