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...

Use of heuristic methods is common to find the solutions to the optimization problems for scientific and real time. Problems such as Travelling Salesman (TSP) require more accurate solution which is tried by various optimization methods. Research in this direction shows the use of Genetic algorithms (GA) as promising candidate and is preferred over other optimization methods. Firstly due to the...

A genetic algorithm (GA) is a robust problem-solving method based on natural selection. Hardware's speed advantage and its ability to parallelize ooer great rewards to genetic algorithms. Speedups of 1{3 orders of magnitude have been observed when frequently used software routines were implemented in hardware by way of reprogrammable eld-pro-grammable gate arrays (FPGAs). Reprogrammability is e...

This work studies the application of Genetic Algorithm (GA) to solve the timetable problem (TTP), a problem that is a highly constrained problem. However, the major disadvantage of GA is that the algorithm uses a tremendous amount of processing time. This paper proposes a method that can reduce the processing time by using Parallel Genetic Algorithm (PGA) on the Beowulf-style cluster computer. ...

This paper examines the exhibition of AI methods for computerized evaluation lymphocytes. proposes a Lymph Diseases Prediction utilizing Genetic Algorithm (GA). In this paper, Computer-Aided Diagnosis framework dependent on Support Vector Machine (SVM) classifier GA highlight determination acquainted with work productivity order precision lymph sickness conclusion. Highlight choice is directed ...

In this paper, the gain in LD-CELP speech coding algorithm is predicted using three neural models, that are equipped by genetic and particle swarm optimization (PSO) algorithms to optimize the structure and parameters of neural networks. Elman, multi-layer perceptron (MLP) and fuzzy ARTMAP are the candidate neural models. The optimized number of nodes in the first and second hidden layers of El...

In this paper, echo cancellation is done using genetic algorithm (GA). The genetic algorithm is implemented by two kinds of crossovers; heuristic and microbial. A new procedure is proposed to estimate the coefficients of adaptive filters used in echo cancellation with combination of the GA with Least-Mean-Square (LMS) method. The results are compared for various values of LMS step size and diff...

With the formation of the competitive electricity markets in the world, optimization of bidding strategies has become one of the main discussions in studies related to market designing. Market design is challenged by multiple objectives that need to be satisfied. The solution of those multi-objective problems is searched often over the combined strategy space, and thus requires the simultaneous...

Transposition is a new genetic operator alternative to crossover and allows a classical GA to achieve better results. This mechanism characterized by the presence of mobile genetic units must be used with the right parameters to enable maximum performance to the GA. This paper presents the results of an empirical study which offers the main guidelines to choose the proper setting of parameters ...