A method for solving satisfiability (SAT) instances is presented. It is based on two basic components: An Epistasis Reducer Algorithm (ERA) and a simple Genetic Algorithm (GA). ERA is implemented by a simulated annealing algorithm (SA), which preprocess the original SAT problem by rearranging the variables to satisfy the condition that the most related ones are in closer positions inside the ch...

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. ...

In this paper, we describe a genetic algorithm (GA) based approach for learning connection weights for an artificial neural network (ANN). We use simulated data sets to compare the GA based approach for learning connection weights against the traditional back-propagation algorithm. Our results indicate that GA based training of ANN has a higher reliability (in terms of over-fitting the training...

In a recent paper [1], it was shown that a new Genetic Algorithm (GA) performed better than basic GA. In the present paper, we will focus an Improved Genetic Algorithm which uses the theory of cosets. The new technique was tested using some benchmark functions [2] which the basic GA performance was not good enough. The main purpose of this paper is to suggest how to use Linear Codes properties ...

background and objectives: rheological characteristics of dough are important for achieving useful information about raw-material quality, dough behavior during mechanical handling, and textural characteristics of products. our purpose in the present research is to apply soft computation tools for predicting the rheological properties of dough out of simple measurable factors. materials and met...

In this paper novel technique for CPU scheduling in real time operating systems by using genetic algorithm (GA) is proposed. Proposed adaptive algorithm is a combination of existing dynamic priority driven algorithm i.e. Earliest Deadline First (EDF) and new genetic algorithm (GA) based scheduling algorithm. First we have developed GA based scheduling algorithm and tested it during both under l...

The concepts of robust classification and intelligently controlling the search process of genetic algorithm (GA) are introduced and integrated with a conventional genetic classifier for development of a new version of it, which is called Intelligent and Robust GA-classifier (IRGA-classifier). It can efficiently approximate the decision hyperplanes in the feature space. It is shown experime...

Identifying software components is a crucial task in software development. There are a number of methods to identify components in the literature; however, the majority of these methods rely on clustering techniques with expert judgment. In contrast to the previous methods, which have used classical clustering techniques, this paper maps the components identification problem to an optimization ...

−We investigate the performance of a classical constrained optimisation (CCO) algorithm and a constrained optimisation Genetic Algorithm (GA) for solving the Bandwidth Allocation for Virtual Paths (BAVP) problem. We compare throughput, fairness and time complexity of GA-BAVP and CCO-BAVP for several node topologies. The results on maximising the throughput obtained with GA-BAVP and CCOBAVP are ...

abstract the muskingum method is frequently used to route floods in hydrology. however, application of the model is still difficult because of the parameter estimation’s. recently, some of heuristic methods have been used in order to estimate the nonlinear muskingum model. this paper presents a efficient heuristic algorithm, simulated annealing, which has been used to estimate the three paramet...