Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic algorithms as function optimizers
Genetic algorithms as function optimizers
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition)
A Short Tutorial on Evolutionary Multiobjective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Polygonal approximation of digital curves using a multi-objective genetic algorithm
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
Deductive sort and climbing sort: New methods for non-dominated sorting
Evolutionary Computation
Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review
Expert Systems with Applications: An International Journal
Mathematical and Computer Modelling: An International Journal
An NSGA-II algorithm for the green vehicle routing problem
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
An efficient genetic algorithm for subgraph isomorphism
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Learning Classification Programs: The Genetic Algorithm Approach
Fundamenta Informaticae
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Genetic algorithms (GAs) are powerful, general purpose adaptive search techniques which have been used successfully in a variety of learning systems. In the standard formulation, GAs maintain a set of alternative knowledge structures for the task to be learned, and improved knowledge structures are formed through a combination of competition and knowledge sharing among the alternative knowledge structures. In this paper, we extend the GA paradigm by allowing multidimensional feedback concerning the performance of the alternative structures. The modified GA is shown to solve a multiclass pattern discrimination task which could not be solved by the unmodified GA.