In this paper, we propose a hybrid algorithm combining two different metaheuristic methods, “Genetic Algorithms (GA)” and “Sperm Swarm Optimization (SSO)”, for the global optimization of multimodal benchmarks functions. The proposed Hybrid Genetic Algorithm Sperm (HGASSO) operates based on incorporates concepts from GA SSO in which generates individuals new iteration not only by crossover mutat...

Abstract: Using robot manipulators for high accuracy applications require precise value of the kinematics parameters. Since measurement of kinematics parameters are usually associated with errors and accurate measurement of them is an expensive task, automatic calibration of robot link parameters makes the task of kinematics parameters determination much easier. In this paper a simple and easy ...

This paper describes a hybrid genetic algorithm for solving instances of the Fixed Channel Assignment Problem (FCAP), a problem that is frequently encountered by designers of mobile telecommunication networks. The hybrid GA manipulates solutions which model networks directly, allowing it to provide realistic assignments for highly constrained problems. Unfortunately, such solutions can be very ...

in this paper, we present a simulated annealing (sa) and a genetic algorithm (ga) based on heuristics for scheduling problem of jobs in virtual cellular manufacturing systems. a virtual manufacturing cell (vmc) is a group of resources that is dedicated to the manufacturing of a part family. although this grouping is not reflected in the physical structure of the manufacturing system, but machin...

A novel version of Ant Colony Optimization (ACO) algorithms for solving continuous space problems is presented in this paper. The basic structure and concepts of the originally reported ACO are preserved and adaptation of the algorithm to the case of continuous space is implemented within the general framework. The stigmergic communication is simulated through considering certain direction vect...

This paper proposes genetic algorithms (GAs) for path planning and trajectory planning of an autonomous mobile robot. Our GA-based approach has an advantage of adaptivity such that the GAs work even if an environment is time-varying or unknown. Therefore, it is suitable for both off-line and on-line motion planning. We first presents a GA for path planning in a 2D terrain. Simulation results on...

We compare Genetic Algorithms (GA) with a functional search method, Very Fast Simulated Reannealing (VFSR), that not only is efficient in its search strategy, but also is statistically guaranteed to find the function optima. GA previously has been demonstrated to be competitive with other standard Boltzmann-type simulated annealing techniques. Presenting a suite of six standard test functions t...

This paper explores & reviews the use of genetic algorithms by various researchers as a solution to discover motifs in molecular sequences. This survey talks about the general GA based procedure for motif discovery & reviews the latest developments in DNA motif finding using Genetic algorithms. Although GA approach has not been applied extensively by researchers as compared to other computation...

In this paper, an immune genetic based algorithm (IGA) for random test pattern generation was proposed. Genetic algorithms (GA) solve many search and optimization problems, effectively. However, they may drop into local optimal solutions; or they may find the optimal solution by low convergence speed. To overcome these problems, we used the immune concept and GA algorithm for random-based test ...