An on line Fuzzy Logic Controller (FLC) realization with Genetic Algorithm (GA) optimization for Automatic Generation Control (AGC) system is developed. By rearranging the multi area AGC system into integration of the SISO cascade loops, the simple FLC Decision Table algorithm could be used in the complex AGC system. This Decision Table looking up algorithm for FLC with GA optimization is suita...

The job-shop scheduling problem (JSSP) is well known as one of the most difficult NP-hard combinatorial optimization problems. Genetic Algorithms (GAs) for solving the JSSP have been proposed, and they perform well compared with other approaches [1]. However, the tuning of genetic parameters has to be performed by trial and error, making optimization by GA ad hoc. To address this problem, Sawai...

The 0/1 Multiple Knapsack Problem is an important class of combinatorial optimization problems, and various heuristic and exact methods have been devised to solve it. Genetic Algorithm (GA) shows good performance on solving static optimization problems. However, sometimes lost of diversity makes GA fail adapt to dynamic environments where evaluation function and/or constraints or environmental ...

When using Genetic Algorithm (GA) to optimize the feature space of pattern classification problems, the performance improved is not only determined by the data set used, but also depend on the classifier. This work compares the improvements acquired by GA optimized feature transformations on several simple classifiers. Some traditional feature transformation techniques, such as Principle Compon...

Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within the framework of competitive learning. This new perspective reveals a number of different possibili...

GAs, which are powerful stochastic optimization techniques are perhaps the most widely known types of evolutionary algorithms. In this paper the Adam-Eve (Like) GA has been considered and its performance has been studied. The basic idea of GAs has been taken from nature; therefore it is reasonable if we expect an improvement in our model of GA, because it is much closer to the evolution of orga...

Integrated scheduling of handling/storage equipment in container terminals is an NP-hard problem which has been studied during past two decades consciously. Genetic algorithms (GAs) have been applied for this optimization problem in many researches. However, the GA is vulnerable to trap in a local optima (results in premature convergence). In this paper a fuzzy logic controller (FLC) is designe...

In this paper we present an enhanced evolutionary algorithm (EA) to solve computationally expensive design optimization problems. In this algorithm we integrate a genetic algorithm (GA) with a local search method to expedite convergence of the GA. We first use a GA to generate a population of data by evaluating real functions, then we construct computationally cheap surrogate models based on th...

Genetic algorithms (GA) have been widely used to solve water resources system optimization. However, when applying GAs to solve large-scale and complex water reservoir system problems, premature convergence is one of the most frequently encountered difficulties and takes a large number of iterations to reach the global optimal solution and the optimization may get stuck at a local optimum. Ther...

in this paper, a multi-product single machine scheduling problem with the possibility of producing defected jobs, is considered. we concern rework in the scheduling environment and propose a mixed-integer programming (mip) model for the problem.  based on the philosophy of just-in-time production, minimization of the sum of earliness and tardiness costs is taken into account as the objective fu...