Test case prioritization (TCP) is aimed at finding an ideal ordering for executing the available test cases to reveal faults earlier. To solve this problem greedy algorithms and meta-heuristics have been widely investigated, but in most cases there is no statistically significant difference between them in terms of effectiveness. The fitness function used to guide meta-heuristics condenses the ...

Scheduling problems play a big role in manufacturing and planning the production for increasing the production efficiency and assigning the resources to operations. Furthermore, in many manufacturing systems there is a physical space between stages that called intermediate buffers. In this study, a model is proposed for minimizing the makespan of a hybrid flow shop scheduling problem with inter...

in this paper, we present an application of evolved neural networks using a real coded genetic algorithm for simulations of monthly groundwater levels in a coastal aquifer located in the shabestar plain, iran. after initializing the model with groundwater elevations observed at a given time, the developed hybrid genetic algorithm-back propagation (ga-bp) should be able to reproduce groundwater ...

A sorting algorithm defines a path in the search space of n! permutations based on the information provided by a comparison predicate. Our generic mutation operator for hybridization, blends a hill-climber and follows the path traced by any sorting algorithm. Our proposal adds search (exploitation) capability to the mutation operator. Mutation requests swaps to construct and test new permutatio...

this paper presents a new mathematical model for a hybrid flow shop scheduling problem with multiprocessor tasks in which sequence dependent set up times and preemption are considered. the objective is to minimize the weighted sum of makespan and maximum tardiness. three meta-heuristic methods based on genetic algorithm (ga), imperialist competitive algorithm (ica) and a hybrid approach of ga a...

This paper describes a detailed investigation of a lattice-based HP (hydrophobic-hydrophilic) model for ab initio protein structure prediction (PSP). The outcome of the simplified HP lattice model has high degeneracy, which could mislead the prediction. The HPNX model was proposed to address the degeneracy problem as well as to avoid the conformational deformity with the hydrophilic (P) residue...

We describe in this paper the evolution of modular neural networks using hierarchical genetic algorithms for pattern recognition. Modular Neural Networks (MNN) have shown significant learning improvement over single Neural Networks (NN). For this reason, the use of MNN for pattern recognition is well justified. However, network topology design of MNN is at least an order of magnitude more diffi...

In this paper a hybrid algorithm based on exploration power of the Genetic algorithms and exploitation capability of Nelder Mead simplex is presented for global optimization of multi-variable functions. Some modifications are imposed on genetic algorithm to improve its capability and efficiency while being hybridized with Simplex method. Benchmark test examples of structural optimization with a...

optimizing the database queries is one of hard research problems. exhaustive search techniques like dynamic programming is suitable for queries with a few relations, but by increasing the number of relations in query, much use of memory and processing is needed, and the use of these methods is not suitable, so we have to use random and evolutionary methods. the use of evolutionary methods, beca...