A New Adaptive Genetic Algorithm and Its Application in the Layout problem

AbstractGenetic algorithm (GA) is a search algorithm based on the theory of Darwin. For the purpose of improving the convergent rate and maintaining the population diversity in GA, this paper presents a new genetic operator called trisecting group and directional selection mechanism (TDGA), in which the worst 2/3 of parent individuals are removed from the population before other manipulations. With 1/3 individuals that are selected randomly from the removed parent individuals, the best 1/3 of the parent individuals is manipulated to reproduce offspring. Simulation results based on 10 test functions show that TDGA is feasible and effective. In addition, inspired by the graph of the function f (x) = e-xc, a new self-adaptation adjusting the tactics of crossover operator and mutation operator (SAGA) is proposed so that individuals with higher fitness cross each other with smaller values of crossover probability, and individuals with lower fitness cross each other with larger values of crossover probability. ...

[1]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Mauricio G. C. Resende,et al.  A biased random-key genetic algorithm for the unequal area facility layout problem , 2015, Eur. J. Oper. Res..

[3]  Ling Wang,et al.  A Hybrid Quantum-Inspired Genetic Algorithm for Multiobjective Flow Shop Scheduling , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[5]  JOSEP DÍAZ,et al.  A survey of graph layout problems , 2002, CSUR.

[6]  Z Xu.  ALMOST SURE STRONG CONVERGENCE OF A CLASS OF GENETIC ALGORITHMS WITH PARENT-OFFSPRINGS COMPETITION , 2002 .

[7]  K.-Y. Gau,et al.  An iterative facility layout algorithm , 1999 .

[8]  Chi-Tai Wang,et al.  A graph-pair representation and MIP model based heuristic for the unequal-area facility layout problem , 2010, The 40th International Conference on Computers & Indutrial Engineering.

[9]  Francisco Herrera,et al.  A taxonomy for the crossover operator for real‐coded genetic algorithms: An experimental study , 2003, Int. J. Intell. Syst..

[10]  Rubén Ruiz,et al.  TWO NEW ROBUST GENETIC ALGORITHMS FOR THE FLOWSHOP SCHEDULING PROBLEM , 2006 .

[11]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[12]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[13]  Hanif D. Sherali,et al.  Applying the sequence-pair representation to optimal facility layout designs , 2007, Oper. Res. Lett..

[14]  Anikó Ekárt,et al.  Genetic algorithms in computer aided design , 2003, Comput. Aided Des..

[15]  M. K. Mayer,et al.  A network parallel genetic algorithm for the one machine sequencing problem , 1999 .

[16]  Carlos A. Coello Coello,et al.  An updated survey of GA-based multiobjective optimization techniques , 2000, CSUR.