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Support Vector Machine (SVM) is a useful technique for data classification with successful applications in different fields of bioinformatics, image segmentation, data mining, etc. A key problem of these methods is how to choose an optimal kernel and how to optimize its parameters in the learning process of SVM. The objective of this study is to propose a Genetic Algorithm approach for parameter optimization...
Particle Swarm Optimization (PSO) algorithms represent a new approach for optimization. In this paper image enhancement is considered as an optimization problem and PSO is used to solve it. Image enhancement is mainly done by maximizing the information content of the enhanced image with intensity transformation function. In the present work a parameterized transformation function is used, which uses...
Entities of the real world require partition into groups based on even feature of each entity. Clusters are analyzed to make the groups homologous and well separated. Many algorithms have been developed to tackle clustering problems and are very much needed in our application area of gene expression profile analysis in bioinformatics. It is often difficult to group the data in the real world clearly...
Biogeography-Based Optimization (BBO) is a new bio-inspired and population based optimization algorithm. The convergence of original BBO to the optimum value is slow. Intelligent Biogeography-Based Optimization (IBBO) technique is a hybrid version of BBO with Bacterial Foraging algorithm (BFA). In this paper, authors integrate the bacterial intelligence feature of BFA to decide the valid emigration...
Differential evolution (DE) algorithm is a heuristic approach that gains more interest in today's research. It finds the true global minimum regardless of the initial parameter values, fast convergence, and using few control parameters. DE algorithm is a population based algorithm like genetic algorithm using similar operators; crossover, mutation and selection. This paper addresses the restrictive...
Ant Colony Optimization (ACO) is more suitable for combinatorial optimization problems. This paper proposes Genetic Evolving Ant Colony Optimization (EACO) method for solving unit commitment (UC) problem. The EACO employs Genetic Algorithm (GA) for finding optimal set of ACO parameters, while ACO solves the UC problem. Problem formulation takes into consideration the minimum up and down time constraints,...
Bio-inspired evolutionary algorithms are probabilistic search methods that simulate the natural biological evolution or the behaviour of biological entities. Such algorithms can be used to obtain near optimal solutions in optimization problems, for which traditional mathematical techniques may fail. This paper does a comparative study of results of five evolutionary algorithms: Genetic Algorithm (GA),...
In this paper we have used a real coded genetic algorithm for finding the global minimum energy conformation of two small molecules viz. Pseudoethane and 1,2,3-trichloro-l-fluoro-propane based on a potential function. Finding the global minimum of this function is very difficult because it has a large number of local minima, which grows exponentially with molecule size. Computational results are obtained...
This paper presents an algorithm for thermal optimization formulation strategies for multi-heat generation of integrated circuit (IC) on printed circuit board (PCB). Weighted-sum approach for multi-objective genetic algorithm (WMOGA) with formulated initial placement and multi-constraints parameters (FIPMCP) are presented. FIPMCP is used for the components selection and components to PCB placement...
In this paper, the genetic algorithm (GA) combined with ray tracing (RT) method is employed to optimize the design of the dielectric radome. The thickness profile of the radome wall is chosen as the optimization parameter and the minimizing of the boresight error (BSE) of the antenna-radome system is the optimization object. The thickness variation in some region of the radome is proposed for optimal...
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