Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
On evolutionary exploration and exploitation
Fundamenta Informaticae
&egr; - Optimal stopping time for genetic algorithms
Fundamenta Informaticae
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Properties of Gray and Binary Representations
Evolutionary Computation
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
Scalability problems of simple genetic algorithms
Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A genetic algorithm for assigning individuals to populations using multi-locus genotyping
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
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We introduce a notion of implicit elitism derived from the mutation operator in genetic algorithms. Probability of mutation less than 1/l (l being the chromosome size) along with probability of crossover less than one induces implicit elitism in genetic search. It implicitly transfers a few chromosomes with above-average fitness unperturbed to the population at next generation, thus maintaining the progress of genetic search. Experiments conducted on one-max and 0/1 knapsack problems testify its efficacy. Implicit elitism in combination with traditional explicit elitism enhances the search capability of genetic algorithms.