Cellular Genetic Algorithms and Local Search for 3-SAT problem on Graphic Hardware

2006 ◽  
Author(s):  
Zhongwen Luo ◽  
Hongzhi Liu
Keyword(s):  
Genetic Algorithms ◽  
Local Search ◽  
Sat Problem ◽  
Graphic Hardware

scholarly journals MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Duygu Yilmaz Eroglu ◽  
H. Cenk Ozmutlu
Keyword(s):  
Genetic Algorithms ◽  
Local Search ◽  
Parallel Machines ◽  
Search Algorithm ◽  
Mixed Integer ◽  
Unrelated Parallel Machines ◽  
Job Splitting ◽  
Mip Models ◽  
Dependent Setup Times ◽  
Genetic Local Search

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes’ random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms.

Effective Memetic Algorithms for VLSI Design = Genetic Algorithms + Local Search + Multi-Level Clustering

2004 ◽  
Vol 12 (3) ◽  
pp. 327-353 ◽  
Author(s):  
Shawki Areibi ◽  
Zhen Yang
Keyword(s):  
Genetic Algorithms ◽  
Local Search ◽  
Optimization Problems ◽  
Memetic Algorithms ◽  
Vlsi Circuit ◽  
Circuit Partitioning ◽  
Placement Problem ◽  
Solution Quality ◽  
Combinatorial Optimization Problems ◽  
Cell Placement

Combining global and local search is a strategy used by many successful hybrid optimization approaches. Memetic Algorithms (MAs) are Evolutionary Algorithms (EAs) that apply some sort of local search to further improve the fitness of individuals in the population. Memetic Algorithms have been shown to be very effective in solving many hard combinatorial optimization problems. This paper provides a forum for identifying and exploring the key issues that affect the design and application of Memetic Algorithms. The approach combines a hierarchical design technique, Genetic Algorithms, constructive techniques and advanced local search to solve VLSI circuit layout in the form of circuit partitioning and placement. Results obtained indicate that Memetic Algorithms based on local search, clustering and good initial solutions improve solution quality on average by 35% for the VLSI circuit partitioning problem and 54% for the VLSI standard cell placement problem.

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