ISSN:
1433-7479
Keywords:
Key words Genetic algorithms
;
optimization
;
numerical methods
;
search methods
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract In Genetic Algorithms mutation probability is usually assigned a constant value, therefore all chromosome have the same likelihood of mutation irrespective of their fitness. It is shown in this paper that making mutation a function of fitness produces a more efficient search. This function is such that the least significant bits are more likely to be mutated in high-fitness chromosomes, thus improving their accuracy, whereas low-fitness chromosomes have an increased probability of mutation, enhancing their role in the search. In this way, the chance of disrupting a high-fitness chromosome is decreased and the exploratory role of low-fitness chromosomes is best exploited. The implications of this new mutation scheme are assessed with the aid of numerical examples.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/s005000000042
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