By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of ...
In this model, word alignment is considered as a multiobjective optimization problem and solved based on the non-dominated sorting genetic algorithm II (NSGA-II), which is one of the best multiobjective evolutionary algorithms (MOEA). There are two advantages of the proposed model based on NSGA...
MultiObjective using Evolutionary Algorithms (2) -- Multi-Objective Optimization,程序员大本营,技术文章内容聚合第一站。
Evolutionary algorithms seem also particularly desirable for solving multi- objective optimization problems because they deal simultaneously with a set of possible solutions (the so-called population) which allows us to find several mem- bers of the Pareto optimal set in a single run of the algori...
Evolutionary computationmultiobjective programminguniform designweighted-sum fitness functionThe weighted sum of objective functions is one of the simplest fitness functions widely applied in evolutionary algorithms (EAs) for multiobjective programming. However, EAs with this fitness function cannot find ...
Abstract—Multiobjective evolutionary algorithms (EAs) that use nondominated sorting and sharing have been criticized mainly for their: 1) O(MN3) computational complexity (where is the number of objectives and is the population size); 2) nonelitism approach; and ...
Abstract—Multiobjective evolutionary algorithms (EAs) that use nondominated sorting and sharing have been criticized mainly for their: 1) O(MN3) computational complexity (where is the number of objectives and is the population size); 2) nonelitism approach; and ...
Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Trans. on Evolutionary Computation 3, 2... Zitzler, E., and L. Thiele (2000), Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach, ...
current state-of-the-art multiobjective evolutionary algorithms (MOEAs) [2]–[4], [12]–[19]. These algorithms treat a MOP as a whole. They do not associate each individual solution with any particular scalar optimization problem. In a scalar objective optimization problem, all the solutions ...
We consider the usage of evolutionary algorithms for multiobjective programming (MOP), i.e. for decision problems with alternatives taken from a real-valued vector space and evaluated according to a vector-valued objective function. Selection mechanisms, possibilities of temporary fitness deterioration, ...