A genetic algorithm approach to periodic railway synchronization
Computers and Operations Research
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Clustering Algorithms
Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems
Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems
Pareto OptimalityGA-Easiness and Deception (Extended Abstract)
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons
Artificial Intelligence Review
Integrated multiobjective optimization and a priori preferences using genetic algorithms
Information Sciences: an International Journal
| Hi-index | 0.00 |
We present a new heuristic method to approximate the set ofPareto-optimal solutions in multicriteria optimizationproblems. We use genetic algorithms with an adaptive selectionmechanism. The direction of the selection pressure is adapted tothe actual state of the population and forces it to explore abroad range of so far undominated solutions. The adaptation isdone by a fuzzy rule-based control of the selection procedureand the fitness function. As an application we present atimetable optimization problem where we used this method toderive cost-benefit curves for the investment into railway nets.These results show that our fuzzy adaptive approach avoids mostof the empirical shortcomings of other multiobjective genetic algorithms.