In this paper a comparison between the single and multi-objective based Optimization techniques including GA, PSO and Hybrid will be presented. The hybrid technique is combined of two attractive evolutionary techniques, Particle Swarm Optimizer (PSO) and Genetic Algorithm (GA) to enhance the search process by improving the diversity, and the convergence toward the preferred solution. This is no...

This paper examines genetic algorithm optimizers in antenna design. Specifically, we evaluate whether GA optimizers are appropriate design techniques for RFID broadband antenna design by looking at current research with GA antennas and deducing whether similar optimizers can be applied to RFID broadband antennas such as bowtie designs.

This paper introduces the notion of using coevolution to adapt the penalty factors of a fitness function incorporated in a genetic algorithm for numerical optimization. The proposed approach produces solutions even better than those previously reported in the literature for other (GA-based and mathematical programming) techniques that have been particularly fine-tuned using a normally lengthy t...

The premature convergence of genetic algorithms (GAs) is the most major factor of slow evolution of GAs. In this paper we propose a novel method to solve this problem through competition of multiple offspring of individuals. Unlike existing methods, each parents in our method generates multiple offspring and then generated multiple offspring compete each other, finally winner offspring become t...

GENE_ARCH is an evolution-based Generative Design System that uses adaptation to shape energy-efficient and sustainable architectural solutions. The system applies goal-oriented design, combining a Genetic Algorithm (GA) as the search engine, with DOE2.1E building simulation software as the evaluation module. The GA can work either as a standard GA or as a Pareto GA, for multicriteria optimizat...

Effective production planning requires models that are capable of accounting for the complexity and uncertainty intrinsic to manufacturing systems. While the identification of a globally optimal plan is desirable, a more important requirement is the ability of a model to produce production plans that are sufficiently realistic to be implemented in practice and are robust to perturbations in the...

Feature selection often used to choose the feature that maximizes the prediction of classification accuracy. Feature selection is one of the most important factor that influence classification accuracy rate. In this paper we proposed the combination of Genetic Algorithm (GA) and Support Vector Machine for feature optimization. In this research we compare the result with K Nearest Neighbor, Deci...

In this paper, the researcher proposes a new evolutionary optimization algorithm that depends on genetic operators such as crossover and mutation, referred to as the bull optimization algorithm (BOA). This new optimization algorithm is called the BOA because the best individual is used to produce offspring individuals. The selection algorithm used in the genetic algorithm (GA) is removed from t...

Genetic algorithms have been extensively used in diierent domains as a means of doing global optimization in a simple yet reliable manner. However, in some realistic engineering design optimization domains it was observed that a simple classical implementation of the GA based on binary encoding and bit mutation and crossover was sometimes ineecient and unable to reach the global optimum. Using ...

Within the framework of Constraint satisfaction and optimization problem (CSOP), we introduce a new optimization distributed method based on Genetic Algorithms (GA). This method consists of agents dynamically created and cooperating in order to solve the problem. Each agent performs its own GA on its own sub-population. This GA is sometimes random and sometimes guided by both the template conce...