In this paper we present a genetic algorithm-based heuristic for solving the set partitioning problem (SPP). The SPP is an important combinatorial optimisation problem used by many airlines as a mathematical model for flight crew scheduling. A key feature of the SPP is that it is a highly constrained problem, all constraints being equalities. New genetic algorithm (GA) components: separate fitn...

A number of algorithms based on the evolutionary processing have been proposed forcommunication networks backbone such as Genetic Algorithm (GA). However, there has beenlittle work on the SWARM optimization algorithms such as Particle Swarm Optimization(PSO) for backbone topology design. In this paper, the performance of PSO on GA isdiscussed and a new algorithm as PSOGA is proposed for the net...

A method based on Electrical Capacitance Tomography (ECT) and an improved Least Squares Support Vector Machine (LS-SVM) is proposed for void fraction measurement of oil-gas two-phase flow. In the modeling stage, to solve the two problems in LS-SVM, pruning skills are employed to make LS-SVM sparse and robust; then the Real-Coded Genetic Algorithm is introduced to solve the difficult problem...

in this paper, we present a genetic algorithm (ga) for optimization of a multi-mode resource constrained time cost trade off (mrctct) problem. the proposed ga, each activity has several operational modes and each mode identifies a possible executive time and cost of the activity. beyond earlier studies on time-cost trade-off problem, in mrctct problem, resource requirements of each execution mo...

A Messy Genetic Algorithm is customized toflnd’optimal many-to-many matches for 2D line segment models. The Messy GA is a variant upon the Standard Genetic Algorithm in which chromosome length can vary. Consequently, population dynamics can be made to drive a relatively efficient and robust search for larger and better matches. Run-times for the Messy GA are as much as an order of magnitude sma...

Multiprocessors have evolved as powerful computing tools for executing dynamic real time tasks. The continual evolutions of the multiprocessor and real-time systems in the last few decades have encouraged the research and development of a new and efficient algorithm for dynamic scheduling of real-time task in multiprocessor systems. This paper proposes a compact study on dynamic real time task ...

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 automatic design of fuzzy rule-based models and classifiers from data is considered. It is recognized that both accuracy and transparency are of major importance and we seek to keep the rule-based models small and comprehensible. An iterative approach for developing such fuzzy rule-based models is proposed. First, an initial model is derived from the data. Subsequently, a real-coded genetic...

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 ...