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...

Genetic Algorithms (GA) has been widely used for logic optimization and synthesis with a view to optimize area, power or testability and for various trades-offs. Traditional logic optimizers such as ESPRESSO targets area minimization only, while present day device scaling demand for extremely low power consumption in VLSI circuits. This paper compares the performances of two types of genetic al...

This paper investigates the nesting issue and the machining path planning issue for improving the sheet metal machining efficiency. The nesting issue is to maximise sheet metal material utilisation ratio by nesting parts of various shapes into the sheet. The path planning issue is to optimise machining sequence so that the total machining path distance and machining time are minimised. This wor...

The job-shop scheduling problem (JSSP) is a well known difficult NP-hard problem. Genetic Algorithms (GAs) for solving the JSSP have been proposed, and they perform well compared with other approaches [1]. However, the tuning of genetic parameters has to be performed by trial and error. To address this problem, Sawai et al. have proposed the Parameter-free GA (PfGA), for which no control parame...

Significant research has been carried out recently to find the optimal path in network routing. Among them, the evolutionary algorithm approach is an area where work is carried out extensively. We in this paper have used particle swarm optimization (PSO) and genetic algorithm (GA) for finding the optimal path and the concept of region based network is introduced along with the use of indirect e...

In this paper, an immune genetic based algorithm (IGA) for random test pattern generation was proposed. Genetic algorithms (GA) solve many search and optimization problems, effectively. However, they may drop into local optimal solutions; or they may find the optimal solution by low convergence speed. To overcome these problems, we used the immune concept and GA algorithm for random-based test ...

We present two sets of tunings that are broadly applicable to conformer searches of isolated molecules using a genetic algorithm (GA). In order to find the most efficient tunings for the GA, a second GA--a meta-genetic algorithm--was used to tune the first genetic algorithm to reliably find the already known a priori correct answer with minimum computational resources. It is shown that these tu...

This paper describes the development of an object-oriented parallel programming environment for genetic algorithms. This work, carried out as part of the ESPRIT III initiative PAPAGENA, intends to promote, develop and demonstrate the effectiveness of genetic algorithm (GA) and parallel genetic algorithm (PGA) techniques in a variety of real-world application domains. Central to this task is the...