Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for Control and Signal Processing
Genetic Algorithms for Control and Signal Processing
Proceedings of the 1st International Conference on Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
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
A Short Tutorial on Evolutionary Multiobjective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Fault Diagnosis: Models, Artificial Intelligence, Applications
Fault Diagnosis: Models, Artificial Intelligence, Applications
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Improving evolutionary multi-objective optimization using genders
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Improving evolutionary multi-objective optimization using genders
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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In solving highly dimensional multi-objective optimization (EMO) problems by evolutionary computations the concept of Pareto-domination appears to be not effective. The paper discusses a new approach to EMO by introducing a concept of genetic genders for the purpose of making distinction between different groups of objectives. This approach is also able to keep diversity among the Pareto-optimal solutions produced.