Genetic Local Search Algorithms for the Travelling Salesman Problem
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Control of Parallel Population Dynamics by Social-Like Behavior of GA-Individuals
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Scheduling by Genetic Local Search with Multi-Step Crossover
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Global characterization of the CEC 2005 fitness landscapes using fitness-distance analysis
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
| Hi-index | 0.00 |
Genetic Algorithms (GAs) are effective approximation algorithms which focus on "hopeful area" in searching process. However, in harder problems, it is often very difficult to maintain a favorable trade-off between exploitation and exploration. All individuals leave the big-valley including the global optimum, and concentrate on another big-valley including a local optimum often. In this paper, we define such a situation on conventional GAs as the " UV-phenomenon", and suggest UV-structures as hard landscape structures that will cause the UV-phenomenon. We propose Innately Split Model (ISM) as a new GA model which can avoid the UV-phenomenon. We apply ISM to Job-shop Scheduling Problem (JSP), which is considered as one of globally multimodal and UV-structural problems. It is shown that ISM surpasses all famous approximation algorithms applied to JSP.