Genetic algorithms have attracted a good deal of interest in the heuristic search community. Yet there are several diierent types of genetic algorithms with varying performance and search characteristics. In this paper we look at 3 genetic algorithms: an elitist simple genetic algorithm, the CHC algorithm and Genitor. One problem in comparing algorithms is that most test problems in the genetic...

Exact mathematical programming techniques such as branch-andbound or dynamic programming and stochastic local search techniques have traditionally been seen as being two general but distinct approaches for the effective solution of combinatorial optimization problems, each having particular advantages and disadvantages. In several research efforts true hybrid algorithms, which exploit ideas fro...

Local search is an emerging paradigm for combinatorial search which has been recently shown to be very e ective for a large number of combinatorial problems. It is based on the idea of navigating the search space by iteratively stepping from one solution to one of its neighbors, which are obtained by applying a simple local change to it. In this paper we present Local++, an object-oriented fram...

this work presents a hybrid method for motif discovery in dna sequences. the proposed method called spso-lk, borrows the concept of chebyshev polynomials and uses the stochastic local search to improve the performance of the basic pso algorithm as a motif finder. the chebyshev polynomial concept encourages us to use a linear combination of previously discovered velocities beyond that proposed b...

Cellular manufacturing system, an application of group technology, has been considered as an effective method to obtain productivity in a factory. For design of manufacturing cells, several mathematical models and various algorithms have been proposed in literature. In the present research, we propose an improved version of discrete particle swarm optimization (PSO) to solve manufacturing cell ...

In this study, to enhance the optimization process, especially in the structural engineering field two well-known algorithms are merged together in order to achieve an improved hybrid algorithm. These two algorithms are Teaching-Learning Based Optimization (TLBO) and Harmony Search (HS) which have been used by most researchers in varied fields of science. The hybridized algorithm is called A Di...

In the GA approach the parameters that influence its performance include population size, crossover rate and mutation rate. Genetic algorithms are suitable for traversing large search spaces since they can do this relatively fast and because the mutation operator diverts the method away from local optima, which will tend to become more common as the search space increases in size. GA’s are base...

Radial Basis Function Neural Networks (RBF NNs) are one of the most applicable NNs in the classification of real targets. Despite the use of recursive methods and gradient descent for training RBF NNs, classification improper accuracy, failing to local minimum and low-convergence speed are defects of this type of network. In order to overcome these defects, heuristic and meta-heuristic algorith...

In this chapter we present a hybrid evolutionary meta-heuristic based on memetic algorithms (MAs) and several local search algorithms. The memetic algorithm is used as the principal heuristic that guides the search and could use any of 16 local search algorithms during the search process. The local search algorithms used in combination with the MA are obtained by fixing either the type of the n...

This paper develops Order Acceptance for an Integrated Production-Distribution Problem in which Batch Delivery is implemented. The aim of this problem is to coordinate: (1) rejecting some of the orders (2) production scheduling of the accepted orders and (3) batch delivery to maximize Total Net Profit. A Mixed Integer Programming is proposed for the problem. In addition, a hybrid meta-heuristic...