Dominance Based Crossover Operator for Evolutionary
Olga Roudenko and Marc Schoenauer
In: PPSN 2004, September 2004, Birmingham.
In spite of the recent quick growth of the Evolutionary
Multi-objective Optimization (EMO) research field, there has been few
trials to adapt the general variation operators to the particular
context of the quest for the Pareto-optimal set. The only exceptions
are some mating restrictions that take in account the distance between
the potential mates -- but contradictory conclusions have been reported.
This paper introduces a particular mating restriction for Evolutionary
Multi-objective Algorithms, based on the Pareto dominance relation:
the partner of a non-dominated individual will be preferably chosen
among the individuals of the population that it dominates. Coupled
with the BLX crossover operator, two different ways of generating
offspring are proposed. This recombination scheme is validated within
the well-known NSGA-II framework on three bi-objective benchmark
problems and one real-world bi-objective constrained optimization
problem. An acceleration of the progress of the population toward the
Pareto set is observed on all problems.