By Ladislav Zjavka (auth.), Jeng-Shyang Pan, Pavel Krömer, Václav Snášel (eds.)
Genetic and Evolutionary Computing
This quantity of Advances in clever platforms and Computing includes accredited papers provided at ICGEC 2013, the seventh overseas convention on Genetic and Evolutionary Computing. The convention this yr was once technically co-sponsored by way of The Waseda college in Japan, Kaohsiung college of utilized technological know-how in Taiwan, and VSB-Technical collage of Ostrava. ICGEC 2013 used to be held in Prague, Czech Republic. Prague is likely one of the most lovely towns on the earth whose magical surroundings has been formed over ten centuries. areas of the best vacationer curiosity are at the Royal course operating from the Powder Tower via Celetna highway to outdated city sq., then throughout Charles Bridge during the Lesser city as much as the Hradcany fort. One are not leave out the Jewish city, and the nationwide Gallery with its effective choice of Czech Gothic paintings, choice of previous eu artwork, and a gorgeous selection of French artwork.
The convention used to be meant as a global discussion board for the researchers and pros in all parts of genetic and evolutionary computing. the most subject matters of ICGEC 2013 integrated clever Computing, Evolutionary Computing, Genetic Computing, and Grid Computing.
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Additional resources for Genetic and Evolutionary Computing: Proceedings of the Seventh International Conference on Genetic and Evolutionary Computing, ICGEC 2013, August 25 - 27, 2013 - Prague, Czech Republic
The proposed genetic scheduling algorithm is presented in Section 3. We describe our experimental results in Section 4. Finally, conclusions are drawn in Section 5. 2 Problem Definition Task scheduling is mapping smaller tasks to multiprocessors. Tasks with data precedence are modeled by a Directed Acyclic Graph (DAG) . The main idea of DAG scheduling is minimizing the makespan which is the overall execution time for all tasks. 1 DAG Modeling A DAG G = (V, E) is depicted in Fig. 1(a), where V is a set of N nodes and E is a set of M directed edges.
On Lunacek bi-Rastrigin without rotation, SwarmRW outperforms all other algorithms, however, it is comparable with ABC. On Lunacek bi-Rastrigin with rotation, PSO–TVAC is the best performer followed by PSO and SwarmRW, with ABC showing the worst performance. This might be due to dependencies among variables, which SwarmRW is unaware of. A niching strategy might be beneﬁcial for SwarmRW on this and similar functions. Overall, SwarmRW has shown successful performance on test functions. It outperforms ABC on all instances, is quite simple to implement and makes use of a single parameter, easing the process of parameter selection.
SwarmRW is validated on test functions and compared to PSO and ABC. Results show improved performance on most of the test functions. Keywords: Continuous Optimization, Swarm Intelligence, Metaheuristic. 1 Introduction A continuous optimization problem is deﬁned by a given objective function. Optimization algorithms search the solution space of such problems in order to ﬁnd a solution, possibly subject to constraints . An unconstrained continuous optimization problem is deﬁned as: minimize f (x), x = (x1 , x2 , .