In this paper, we consider a variety of random parameters of genetic algorithms based on some benchmark functions and traveling salesman problem (TSP). We have analyzed parameters of the genetic algorithm such as population size, crossover probability and mutation probability. The experiments have shown that we cannot propose a uniform model for the parameters of a genetic algorithm. However increasing of population size can reduce genetic algorithm iterations but crossover probability and also mutation probability strongly depend on benchmark functions
Genetic algorithm is a method of optimization based on the concepts of natural selection and genetic...
[[abstract]]The probabilistic traveling salesman problem (PTSP) is a topic of theoretical and practi...
[[abstract]]The probabilistic traveling salesman problem (PTSP) is a topic of theoretical and practi...
Tuning evolutionary algorithms is a persistent challenge in the field of evolutionary computing. The...
Abstract-Genetic algorithm (GA) is a meta-heuristic inspired by the efficiency of natural selection ...
ABSTRACT: In this paper, we describe the use of advanced statistical design in the screening experim...
Genetic algorithms (GAs) are multi-dimensional and stochastic search methods, involving complex in-t...
In this paper, we applied different operators of crossover and mutation of the genetic algorithm to ...
In this study, we provide a new taxonomy of parameters of genetic algorithms (GA), structural and nu...
Abstract — Genetic Algorithms are the population based search and optimization technique that mimic ...
Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort t...
Abstract — Genetic Algorithms are the population based search and optimization technique that mimic ...
Abstract(i) We investigate spectral and geometric properties of the mutation-crossover operator in a...
AbstractThis paper presents stochastic models for two classes of Genetic Algorithms. We present impo...
Abstract — This paper presents the literature survey review of Travelling Salesman Problem (TSP). TS...
Genetic algorithm is a method of optimization based on the concepts of natural selection and genetic...
[[abstract]]The probabilistic traveling salesman problem (PTSP) is a topic of theoretical and practi...
[[abstract]]The probabilistic traveling salesman problem (PTSP) is a topic of theoretical and practi...
Tuning evolutionary algorithms is a persistent challenge in the field of evolutionary computing. The...
Abstract-Genetic algorithm (GA) is a meta-heuristic inspired by the efficiency of natural selection ...
ABSTRACT: In this paper, we describe the use of advanced statistical design in the screening experim...
Genetic algorithms (GAs) are multi-dimensional and stochastic search methods, involving complex in-t...
In this paper, we applied different operators of crossover and mutation of the genetic algorithm to ...
In this study, we provide a new taxonomy of parameters of genetic algorithms (GA), structural and nu...
Abstract — Genetic Algorithms are the population based search and optimization technique that mimic ...
Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort t...
Abstract — Genetic Algorithms are the population based search and optimization technique that mimic ...
Abstract(i) We investigate spectral and geometric properties of the mutation-crossover operator in a...
AbstractThis paper presents stochastic models for two classes of Genetic Algorithms. We present impo...
Abstract — This paper presents the literature survey review of Travelling Salesman Problem (TSP). TS...
Genetic algorithm is a method of optimization based on the concepts of natural selection and genetic...
[[abstract]]The probabilistic traveling salesman problem (PTSP) is a topic of theoretical and practi...
[[abstract]]The probabilistic traveling salesman problem (PTSP) is a topic of theoretical and practi...