Abstract
Compared with natural systems, Genetic Algorithms have a limited adaptive capacity, i.e. they get quite frequently trapped at local optima and they are poor at tracking moving optima in dynamic environments. This paper describes a general, formal model of co-evolution, the Linear Model of Symbiosis, that allows for the concise, unified expression of all types of co-evolutionary relations studied in ecology. Experiments on several difficult problems support our assumption that the addition of the Linear Model of Symbiosis to a canonical Genetic Algorithm can remedy the above shortcomings.
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Morrison, J., Oppacher, F. (1999). A General Model of Co-evolution for Genetic Algorithms. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6384-9_44
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DOI: https://doi.org/10.1007/978-3-7091-6384-9_44
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83364-3
Online ISBN: 978-3-7091-6384-9
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