Hybrid metaheuristics have received considerable interest in recent years. Since several years ago, a wide variety of hybrid approaches have been proposed in the literature including the new GA-EDA approach. We have design and implemented an extension to this GA-EDA approach, based on statistical significance tests. This approach had allowed us to make an study of the balance of diversification...

Two well known optimization algorithms, the Genetic Algorithm (GA) and the Simulated Annealing Algorithm (SAA), are investigated for IIR adaptive phase equalizers. For non-convex error surfaces, gradient-based algorithms often fail to find the global optimum. This work compares the ability of the GA and the SAA to achieve the global minimum solution for multi-order all-pass adaptive filters to ...

The aim of this work is to present some alternatives to improve the performance of an Evolutionary Algorithm applied to the problem known as the Oil Collecting Vehicle Routing Problem. Some proposals based on the insertion of Local Search and Data Mining modules in a Genetic Algorithm (GA) are presented. Four algorithms were developed: a Genetic Algorithm, a Genetic Algorithm with a Local Searc...

In this paper, optimization of the backstepping controller parameters in a grid-connected single-phase inverter is studied using Imperialist competitive algorithm (ICA), Genetic Algorithm (GA) and Particle swarm optimization (PSO) algorithm. The controller is developed for the system based on state-space averaged model. By selection of a suitable Lyapunov function, stability of the proposed con...

Using a Genetic Algorithm (GA), an artificial intelligence technique, this study proposes an user-interactive dynamic portfolio selection strategy using a decision support system that will generate an optimal investment mix of assets based on user selection by maximizing the return of the Sharpe Ratio, a measure of the excess return received on a portfolio for the increase of volatility by acqu...

In this paper the problem of image segmentation is addressed using the notion of thresholding. A new approach based on Genetic Algorithm (GA) is proposed for selection of threshold from the histogram of images. Specifically GA based crowding algorithm is proposed for determination of the peaks and valleys of the histogram. Experimental results are provided for histogram with bimodal feature, ho...

In this paper, we design a genetic algorithm based on the Linkage Identiication by Nonlinearity Check (LINC) procedure proposed in a previous report (Munetomo & Goldberg, 1998). The resulting LINC-GA performs genetic algorithms inside the linkage groups obtained by the LINC procedure to nd candidates of building blocks and then mixes them to obtain optimal or suboptimal solutions. The procedure...

In this paper we present a method based on preference relations for transforming non–crisp (qualitative) relationships between objectives in multi–objective optimisation into quantitative attributes (i.e. numbers). This is integrated with two multi–objective Genetic Algorithms: weighted sums GA and a method for combining the Pareto method with weights. Examples of preference relations applicati...

This paper details an application of genetic algorithms (GA) developed for the optimisation of fixture locator positioning for 3D freeform components. Based on the information of the workpiece, a genetic algorithm based approach is applied to determine the most statically stable fixture configuration from a large number of possible candidates. The preliminary implementation is introduced to dem...

This paper uses the simple structure of the knapsack problem to study the issues of mapping and representation for genetic algorithms. Two genetic algorithms using different mappings were implemented to solve the problem. In one of these neither the order or position of genes is significant. Both of the genetic algorithms perform well on the problem, and we attribute the divergent parameter set...