2002/05/23 by M. Gutowski, Marek W. Gutowski, Gutowski, Marek W.
Computer Science · Physics and Astronomy · #Data Analysis #Data Structures and Algorithms (cs.DS) #Evolutionary Algorithms and Applications #F.2.1 #FOS: Computer and information sciences #FOS: Physical sciences #G.1.6 #I.1.2 #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #Statistics and Probability (physics.data-an) #cs.DS #cs.NE #physics.data-an
paper · pdf · doi:10.48550/arxiv.cs/0205061
Submitted to the workshop on evolutionary algorithms, Krakow (Cracow), Poland, Sept. 30, 2002, 6 pages, no figures, LaTeX 2.09 requires kaeog.sty (included)
arxiv created 2002/05/23 · openalex publication_date 2002/05/23 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Over a quarter of century after the invention of genetic algorithms and miriads of their modifications, as well as successful implementations, we are still lacking many essential details of thorough analysis of it's inner working. One of such fundamental questions is: how many generations do we need to solve the optimization problem? This paper tries to answer this question, albeit in a fuzzy way, making use of the double helix concept. As a byproduct we gain better understanding of the ways, in which the genetic algorithm may be fine tuned.