Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Evolving Objects: A General Purpose Evolutionary Computation Library
Selected Papers from the 5th European Conference on Artificial Evolution
Design of multi-objective evolutionary algorithms: application to the flow-shop scheduling problem
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Optimizing communications in vehicular ad hoc networks using evolutionary computation and simulation
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
On the Integration of a TSP Heuristic into an EA for the Bi-objective Ring Star Problem
HM '08 Proceedings of the 5th International Workshop on Hybrid Metaheuristics
Information Processing Letters
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Metaheuristics for the bi-objective ring star problem
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Path-guided mutation for stochastic pareto local search algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Opt4J: a modular framework for meta-heuristic optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Using an evolutionary algorithm to optimize the broadcasting methods in mobile ad hoc networks
Journal of Network and Computer Applications
A call for collaborative landscape analysis
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Liger: an open source integrated optimization environment
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Pareto-based multiobjective AI planning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
| Hi-index | 0.00 |
This paper presents ParadisEO-MOEO, a white-box object-oriented generic framework dedicated to the flexible design of evolutionary multi-objective algorithms. This paradigm-free software embeds some features and techniques for Pareto-based resolution and aims to provide a set of classes allowing to ease and speed up the development of computationally efficient programs. It is based on a clear conceptual distinction between the solution methods and the multi-objective problems they are intended to solve. This separation confers a maximum design and code reuse. ParadisEO-MOEO provides a broad range of archive-related features (such as elitism or performance metrics) and the most common Pareto-based fitness assignment strategies (MOGA, NSGA, SPEA, IBEA and more). Furthermore, parallel and distributed models as well as hybridization mechanisms can be applied to an algorithm designed within ParadisEO-MOEO using the whole version of ParadisEO. In addition, GUIMOO, a platform-independant free software dedicated to results analysis for multi-objective problems, is briefly introduced.