Software Practice and ExperienceE. Falkenauer, "Genetic Algorithms and Grouping Problems", John Wiley & Sons, 1998.Genetic Algorithms and Grouping Problems. Falkenauer E. . 1998Emanuel Falkenauer. Genetic Algor
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...
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 ...
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 ...
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-...
3. GGA Algorithm The Grouping Genetic Algorithm[2](GGA)10 , is one class of evolutionary algorithms which is modified specifically to cope with grouping problems i.e. the problems that in them some items should be assigned to a set of predefined groups. So, in GGA, coding scheme,...
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...
solutions for 88.5% of these instances can be obtained with practical optimization times while solving the rest of the problems with no more than one extra bin. When the results are compared with the existing state-of-the-art heuristics, the developed parallel hybrid groupinggenetic algorithmscan ...