An improved genetic algorithm for solving the graph planarization problem is presented. The improved genetic algorithm which is designed to embed a graph on a plane, performs crossover and mutation conditionally instead of probability. The improved genetic algorithm is verified by a large number of simulation runs and compared with other algorithms. The experimental results show that the improv...

in this paper, a new genetic algorithm (ga) is presented for solving the multi-mode resource-constrained project scheduling problem (mrcpsp) with minimization of project makespan as the objective subject to resource and precedence constraints. a random key and the related mode list (ml) representation scheme are used as encoding schemes and the multi-mode serial schedule generation scheme (mssg...

This paper extends the theoretical analysis of the Adaptive Reservoir Genetic Algorithm (ARGA), a variant of a Genetic Algorithm (GA) proposed by the authors in [4]. We show that ARGA visits the global optimum after a finite number of iterations with probability one, regardless of the initialization of the population. Key-Words: Genetic Algorithms, convergence analysis, adaptive control of search.

We study the benefits of Genetic Algorithms, in particular the crossover operator, in constructing experimental designs that are D-optimal. To this purpose, we use standard Monte Carlo algorithms such as DETMAX and k-exchange as the mutation operator in a Genetic Algorithm. Compared to the heuristics, our algorithms are slower but yield better results. Key-Words: Genetic Algorithm, Memetic Algo...

A hybrid Neural-Genetic algorithm (NG) is presented for FPGA Segmented Channel Routing Problems (FSCRP). The NG algorithm consists in a Hopfield Neural Network (HNN) which manages the problem constraints, hybrided with a Genetic Algorithm (GA) for improving the solutions obtained. Six hard FSCRP instances have been generated in order to test the performance of the NG algorithm. Key-Words: FPGAs...