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Erschienen in:
2023 | OriginalPaper | Buchkapitel
Genetic Algorithm with Evolutionary Jumps
verfasst von : Hafsa Farooq, Daniel Novikov, Akshay Juyal, Alexander Zelikovsky
Erschienen in: Bioinformatics Research and Applications
Verlag: Springer Nature Singapore
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Abstract
It has recently been noticed that dense subgraphs of SARS-CoV-2 epistatic networks correspond to future unobserved variants of concern. This phenomenon can be interpreted as multiple correlated mutations occurring in a rapid succession, resulting in a new variant relatively distant from the current population. We refer to this phenomenon as an evolutionary jump and propose to use it for enhancing genetic algorithm. Evolutionary jumps were implemented using C-SNV algorithm which find cliques in the epistatic network. We have applied the genetic algorithm enhanced with evolutionary jumps (GA+EJ) to the 0–1 Knapsack Problem, and found that evolutionary jumps allow the genetic algorithm to escape local minima and find solutions closer to the optimum.