Falkenauer, E (1998) Genetic algorithms and grouping problems. Wiley, New YorkFalkenauer E. Genetic algorithms and grouping problems[M]. England:John Wiley & Sons Ltd, 1998.Genetic Algorithms and Grouping Problems - Falkenauer - 1998 () Citation Context ...Therefore, the R chromosomes with ...
Genetic algorithms (GAs) are search methods based on principles of natural selection and genetics (Fraser, 1957;Bremermann, 1958;Holland, 1975). We start with a brief introduction to simple genetic algorithms and associated terminology. to check access. References Asoh, H. and Mühlenbein, H., ...
Holland JH (1995) Adaptation in natural and artificial systems. MIT Press, Cambridge Google Scholar Falkenauer E (1998) Genetic algorithms and grouping problems. Wiley, New York Google Scholar Srinivas M, Patnaik LM (1994) Genetic algorithms: a survey. IEEE Comput, vol 27,Issue 6, 17–26...
Battaglia, Genetic algorithmsand clustering: An application to Fisher’s iris data, in S.Borra, R. Rocci, M. Vichi, & M. Schader (Eds.), Advances inclassification and data analysis (New York: Springer-Verlag,2001), 109–118. [13] S. Paterlini, S. Favaro, & T. Minerva, Genetic appr...
Among these metaheuristics, Genetic Algorithms (GA) are very popular (Goldberg, 1989). GAs explore the search space by using the Darwinian principles of natural selection. Due to their success in solving a wide variety of problems, several researchers have applied GA on GPP. However, only a ...
That is, the linguistics people are looking at patterns in language, and that's what bioinformatics people do--looking for patterns within sequences of DNA or protein. Creation of databases Development of algorithms and statistics Analysis of data and interpretation Track 15: Reproductive Genetics/Pr...
A hybrid grouping genetic algorithm for the inventory routing problem with multi-tours of the vehicleMeta-heuristic and heuristics algorithmsIn this paper we analyse the inventory routing model as described recently by Aghezaff et al. in which the work of each vehicle can be organized in multi-...
grouping genetic algorithm (GGA)An effective method based on the Genetic Algorithms is proposed to solve the Handicapped Person Transportation problem, which is a real-life application for pickup and delivery problems. In these problems, vehicles have to transport (clients, loads, etc.,) from ...
In this paper, we introduce Genetic State-Grouping Algorithm (GSGA) enhanced by deep reinforcement learning. It is an integration of genetic algorithms and State Grouping, our genuine method, in pursuit of increasing deep reinforcement learning’s learning effectiveness in terms of time and resource...
The algorithm has been used for solving several optimization problems and its ability to find optimized solutions makes it one of the most used algorithms. The main purpose of proposed algorithm is to make the balance between maintenance costs (i.e. direct and indirect) and down time cost ...