NCGA : Neighborhood Cultivation Genetic Algorithm for Multi-Objective Optimization Problems
Shinya Watanabe, Tomoyuki Hiroyasu, Mitsunori Miki · 2002
In this paper, a new genetic algorithm for multi-objective optimization problems is introduced. That is called ”Neighborhood Cultivation GA (NCGA)”. In the recent studies such as SPEA2 or NSGA-II, it is demonstrated that some mechanisms are important; the mechanisms of placement in an archive of the excellent solutions, sharing without parameters, assign of fitness, selection and reflection the archived solutions to the search population. NCGA includes not only these mechanisms but also the neighborhood crossover. The comparison of NCGA with SPEA2 and NSGA-II by some test functions shows that NCGA is a robust algorithm to find Pareto-optimum solutions. Through the comparison between the case of using neighborhood crossover and the case of using normal crossover in NCGA, the effect of neighborhood crossover is made clear.