Abstract
The genetic algorithm is a well-known method of evolutionary-based optimization. It mimics evolving processes of biological organisms. Yet, one of the aspects of nature-based processes is not followed, i.e., continuous changes of organisms that results in a population containing individuals of that belong to different generations at the same time. In this paper, we present a new approach to model operations of genetic algorithms based on the idea of stream processing. The new algorithm—called On-Line Generation-less Genetic Algorithm (olgga)—allows for: continuous evaluation of individuals (chromosomes); variability in a population of individuals that eliminates boundaries between generations; a fast adaptation to changes in objectives and optimization environment, and a dynamic operational structure that facilitates parallelization of the algorithm. We present a description of the proposed algorithm and its application to a knapsack optimization problem.
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Buss, A., Reformat, M.Z., Musilek, P. (2022). olgga: An On-Line Generation-Less Genetic Algorithm. In: Harmati, I.Á., Kóczy, L.T., Medina, J., Ramírez-Poussa, E. (eds) Computational Intelligence and Mathematics for Tackling Complex Problems 3. Studies in Computational Intelligence, vol 959. Springer, Cham. https://doi.org/10.1007/978-3-030-74970-5_16
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DOI: https://doi.org/10.1007/978-3-030-74970-5_16
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