Multi-objective genetic algorithm and its applications to flowshop scheduling
Computers and Industrial Engineering
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
Fine-Grained Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A Variant of Evolution Strategies for Vector Optimization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Many-Objective Optimization for Knapsack Problems Using Correlation-Based Weighted Sum Approach
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Simultaneous use of different scalarizing functions in MOEA/D
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
How to choose solutions for local search in multiobjective combinatorial memetic algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Effects of the existence of highly correlated objectives on the behavior of MOEA/D
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
GRACE: a generational randomized ACO for the multi-objective shortest path problem
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
| Hi-index | 0.00 |
When we try to implement a multi-objective genetic algorithm (MOGA) with variable weights for finding a set of Pareto optimal solutions, one difficulty lies in determining appropriate search directions for genetic search. In our MOGA, a weight value for each objective in a scalar fitness function was randomly specified. Based on the fitness function with the randomly specified weight values, a pair of parent solutions are selected for generating a new solution by genetic operations. In order to find a variety of Pareto optimal solutions of a multi-objective optimization problem, weight vectors should be distributed uniformly on the Pareto optimal surface. In this paper, we propose a proportional weight specification method for our MOGA and its variants. We apply the proposed weight specification method to our MOGA and a cellular MOGA for examining its effect on their search ability.