On binary unbiased operators returning multiple offspring

Bulanova, Nina; Buzdalov, Maxim

doi:10.1145/3067695.3082505

Cited by 1 publication

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“…Similar definitions exist for operators of higher arities, as well as for operators returning multiple offspring [7]. This view enables a unified framework and a single-entry implementation for support of arbitrary operators of a given arity.…”

Section: Preliminaries and Related Workmentioning

confidence: 91%

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Improving Time and Memory Efficiency of Genetic Algorithms by Storing Populations as Minimum Spanning Trees of Patches

Buzdalov

2023

Proceedings of the Companion Conference on Genetic and Evolutionary Computation

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In many applications of evolutionary algorithms the computational cost of applying operators and storing populations is comparable to the cost of fitness evaluation. Furthermore, by knowing what exactly has changed in an individual by an operator, it is possible to recompute fitness value much more efficiently than from scratch. The associated time and memory improvements have been available for simple evolutionary algorithms, few specific genetic algorithms and in the context of gray-box optimization, but not for all algorithms, and the main reason is that it is difficult to achieve in algorithms using large arbitrarily structured populations.This paper makes a first step towards improving this situation. We show that storing the population as a minimum spanning tree, where vertices correspond to individuals but only contain meta-information about them, and edges store structural differences, or patches, between the individuals, is a viable alternative to the straightforward implementation. Our experiments suggest that significant, even asymptotic, improvements -including execution of crossover operators! -can be achieved in terms of both memory usage and computational costs.

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Section: Preliminaries and Related Workmentioning

confidence: 91%

“…For instance, the standard bit mutation with mutation rate p samples ℓ from a binomial distribution with parameters n and p. Similarly, any crossover operator can be represented as follows [7,10,21,22]:…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

Improving Time and Memory Efficiency of Genetic Algorithms by Storing Populations as Minimum Spanning Trees of Patches

Buzdalov

2023

Proceedings of the Companion Conference on Genetic and Evolutionary Computation

Self Cite

View full text Add to dashboard Cite

show abstract

On binary unbiased operators returning multiple offspring

Cited by 1 publication

References 8 publications

Improving Time and Memory Efficiency of Genetic Algorithms by Storing Populations as Minimum Spanning Trees of Patches

Improving Time and Memory Efficiency of Genetic Algorithms by Storing Populations as Minimum Spanning Trees of Patches

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