Mesh puppetry: cascading optimization of mesh deformation with inverse kinematics
ACM SIGGRAPH 2007 papers
An adaptable transport protocol based on Genetic Algorithms
International Journal of Information and Communication Technology
Enhanced Cooperative Co-evolution Genetic Algorithm for Rule-Based Pattern Classification
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Adaptive primal-dual genetic algorithms in dynamic environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Exploiting molecular dynamics for multi-objective optimization
Expert Systems with Applications: An International Journal
Ordered Incremental Multi-Objective Problem Solving Based on Genetic Algorithms
International Journal of Applied Evolutionary Computation
Hi-index | 0.00 |
This paper presents a new genetic algorithm approach to multiobjective optimization problems-incremental multiple objective genetic algorithms (IMOGA). Different from conventional MOGA methods, it takes each objective into consideration incrementally. The whole evolution is divided into as many phases as the number of objectives, and one more objective is considered in each phase. Each phase is composed of two stages. First, an independent population is evolved to optimize one specific objective. Second, the better-performing individuals from the single-objective population evolved in the above stage and the multiobjective population evolved in the last phase are joined together by the operation of integration. The resulting population then becomes an initial multiobjective population, to which a multiobjective evolution based on the incremented objective set is applied. The experiment results show that, in most problems, the performance of IMOGA is better than that of three other MOGAs, NSGA-II, SPEA, and PAES. IMOGA can find more solutions during the same time span, and the quality of solutions is better.