Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
A Multi-objective Approach to Constrained Optimisation of Gas Supply Networks: the COMOGA Method
Selected Papers from AISB Workshop on Evolutionary Computing
Hi-index | 0.00 |
Evolutionary algorithms (EAs) are always be a good choice to solve multi-objective problems (MLPs). EA use ranking schemes to solve MLP's. Most of ranking schemes use only objective values to rank the solution where as some use the condition in which the problem arises to rank the solutions. In this paper we have proposed one ranking method which use situation as well as objective values to rank the solutions. This method is currently being tested on standard single and multi objective problems, the early results are encouraging.