This paper discusses a genetic algorithm (GA) based method for automatically tuning mobile robot motion controllers. The genetic algorithm evolves a controller that is optimised for a given performance measure. Genetic algorithms require a mapping from the genetic code to an implementation. This translation between the chromosome and the implementation allows the use of standard GA libraries, h...

The multivariate exponentially weighted moving average (MEWMA) control chart is one of the best statistical control chart that are usually used to detect simultaneous small deviations on the mean of more than one cross-correlated quality characteristics. The economic design of MEWMA control charts involves solving a combinatorial optimization model that is composed of a nonlinear cost function ...

We have been investigating on the generation of input pairs for combinational digital circuits that cause the maximum power consumption on them. This work shows the results we obtained using genetic algorithms (GA). We show a GA that runs almost in the same time that speed up simulated annealing algorithms and give better results than them for half of the ISCAS85 benchmark circuits.

in many real world assembly line systems which the work-piece is of large size more than one worker work on the same work-piece in each station. this type of assembly line is called multi-manned assembly line (mal). in the classical multi-manned assembly line balancing problem (malbp) the objective is to minimize the manpower needed to manufacture one product unit. apart from the manpower, othe...

According to the results of previous research, the genetic algorithm (GA) is a good technique for finding near-global optimal solutions for group optimization. There are three main factors that affect the performance of genetic algorithms: the operator selections; parameter settings and chromosome structure representation, which have previously been left to the user. Dynamic parameter setting i...

This is a shorter version of an article presented in: Fundamenta Informaticae, vol. 28 (3, 4), pp: 423-430. IOS Press, 1996. A lot of research on genetic algorithms theory is concentrated on classical, binary case. However, there are many other types of useful genetic algorithms (GA), e.g. tree-based (genetic programming), or order-based ones. This paper shows, that many of classical results ca...

In this paper, it has been integrated Nash Equilibrium solution of Game Theory with Genetic Algorithms (GA) to optimize performance of a job scheduler, in order to simulate topology and sizing of Analog Electrical Circuits simulation. We proposed a new method for performance problems solving of Genetic Algorithms applied to Electronic Design Automation (EDA) simulator tool optimization. This op...

The integration of genetic algorithms (GAs) and tabu search is one of traditional problems in function optimization in the GA literature. However, most proposed methods have utilized genetic algorithms to explore global candidates and tabu search to exploit local optimal points. Unlike such methods so far, this paper proposes a new algorithm to directly store individuals into multiple tabu list...

Genetic Algorithms (GAs) are the search algorithms and optimization techniques based on the mechanics of natural selection and natural genetics. They sort out interesting areas of a space quickly but without guaranteeing more convergence. So GA may be mixed with various local problem-specific search techniques to form a hybrid that will combine the globality and parallelism of GA with more conv...