Proceedings of the third international conference on Genetic algorithms
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
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Some Guidelines for Genetic Algorithms with Penalty Functions
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Operators in a Dual Genetic Algorithm
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
A sequential niche technique for multimodal function optimization
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
| Hi-index | 0.00 |
This paper proposes an exploratory multi-objective evolutionary algorithm (EMOEA) that makes use of the integrated features of tabu search and evolutionary algorithms for effective multi-objective optimization. It incorporates a tabu list and tabu constraint for individual examination and preservation to enhance the evolutionary search diversity in multi-objective optimization, which subsequently helps to avoid the search from trapping in local optima and at the same time, promotes the evolution towards the global Pareto-front. A novel method of lateral interference is also suggested, which is capable of distributing non-dominated individuals uniformly along the discovered Pareto-front at each generation. Unlike existing niching/sharing methods, lateral interference can be performed without the need of any parameter setting and can be flexibly applied in either parameter or objective domain depending on the nature of the optimization problem involved. The proposed features are experimented in order to illustrate their behavior and usefulness in the algorithm.