Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A note on the performance of genetic algorithms on zero-one knapsack problems
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
PARA '02 Proceedings of the 6th International Conference on Applied Parallel Computing Advanced Scientific Computing
An application of adaptive genetic algorithm in financial knapsack problem
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Combination of Global and Local Search for Real Function Optimization
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Adaptive genetic algorithm with mutation and crossover matrices
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Expert Systems with Applications: An International Journal
Complex energy landscape mapping by histogram assisted genetic algorithm
Proceedings of the 12th annual conference on Genetic and evolutionary computation
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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A new approach to evolutionary computation with mutation only is developed by the introduction of the mutation matrix. The method of construction of the mutation matrix is problem independent and the selection mechanism is achieved implicitly by individualized and locus specific mutation probability based on the information on locus statistics and fitness of the population, and traditional genetic algorithm with selection and mutation can be treated a special case. The mutation matrix is parameter free and adaptive as the mutation probability is time dependent, and captures the accumulated information in the past generations. Three methodologies, mutation by row, mutation by column, and mutation by mixing row and column are introduced and tested on the resource allocation problem of the zero/one knapsack problem, showing high efficiency in speed and high quality of solution compared to other traditional methods.