We present a novel application of genetic algorithms to the problem of aligning multiple biological sequences through the optimization of guide trees. Individual guide trees are represented as coalescing binary trees which provide for efficient and meaningful crossover and mutation operations. We hypothesize that our technique avoids the limitations of other heuristic tree-building techniques, ...

An evolutionary algorithm that utilizes a-priori problem specific information and allows intuitive representation of the problem design variables is proposed. A technique for conditioning the components of the fitness statement using ranking and a graphical method for monitoring components of the rank based fitness function are presented. By utilizing generationally dependant non-linear rank ba...

The Vehicle Routing Problem (VRP) is a NP-hard and Combinatorial optimization problem. Combinatorial optimization problem can be viewed as searching for best element in a set of discrete items, which can be solved using search algorithm or meta heuristic. In this work, VRP is solved using population based search algorithm, Particle Swarm Optimization (PSO) with crossover and mutation operators....

The common problem of multiprocessor scheduling can be defined as allocating a task graph in a multiprocessor system so that schedule length can be improved. Task scheduling in multiprocessor system is a NP-complete problem. A number of heuristic methods have been cultivated that achieve partial solutions in less than the minimum computing time. Genetic algorithms have obtained much awareness a...

This article presents an evolutionary algorithm (EA) for the capacitated minimum spanning tree problem occurring in telecommunication applications. The EA encodes a solution by a predecessor vector indicating for each node the preceding node at the path to the given central root node. Initialization, crossover, and mutation operators were specifically designed to provide strong locality and to ...

A single machine n-job scheduling problem is examined to minimize sum of absolute deviations of completion times from a common due date. Simple and hybrid genetic Algorithms are developed by investigating basic operators for the applications of job sequencing problems. For the simple genetic algorithm two heuristic crossover schemes: Algorithm VASX and Algorithm VADX are developed based on impo...

Efficient scheduling algorithm is required to use the grid resources properly. List scheduling heuristic algorithms are to schedule the application programs and produces sub optimal solutions. An evolutionary based scheduling algorithm will produce an optimal solution. The proposed algorithm, namely, “Grid Scheduling Strategy using GA” (GSSGA) adopts the genetic algorithm to solve the dependent...

Scheduling problems are important NP-hard problems. Genetic algorithms can provide good solutions for such optimization problems. In this paper, we present a genetic algorithm to solve the single machine total weighted tardiness scheduling problem, which is a strong NP-hard problem. The algorithm uses the natural permutation representation of a chromosome, heuristic dispatching rules combined w...

The task of global optimization is to find a point where the objective function obtains its most extreme value. Differential evolution (DE) is a population-based heuristic approach that creates new candidate solutions by combining several points of the same population. The algorithm has three parameters: amplification factor of the differential variation, crossover control parameter and populat...

The objective of this paper is to propose three modified versions of the Gravitational Search Algorithm for continuous optimization problems. Although the Gravitational Search Algorithm is a recently introduced promising memory-less heuristic but its performance is not so satisfactory in multimodal problems particularly during the later iterations. With a view to improve the exploration and exp...