Hybrid Genetics Algorithms for Multiple Sequence Alignment

2016 ◽  
pp. 346-366 ◽  
Author(s):  
John Tsiligaridis
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Traveling Salesman Problem ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Hybrid Genetic Algorithm ◽  
Traveling Salesman ◽  
Divide And Conquer ◽  
Multiple Sequence ◽  
The Traveling Salesman Problem

The purpose of this chapter is to present a set of algorithms and their efficiency for the consistency based Multiple Sequence Alignment (MSA) problem. Based on the strength and adaptability of the Genetic Algorithm (GA) two approaches are developed depending on the MSA type. The first approach, for the non related sequences (no consistency), involves a Hybrid Genetic Algorithm (GA_TS) considering also Tabu Search (TS). The Traveling Salesman Problem (TSP) is also applied determining MSA orders. The second approach, for sequences with consistency, deals with a hybrid GA based on the Divide and Conquer principle (DCP) and it can save space. A consistent dot matrices (CDM) algorithm discovers consistency and creates MSA. The proposed GA (GA_TS_VS) also uses TS but it works with partitions. In conclusion, GAs are stochastic approaches that are proved very beneficial for MSA in terms of their performance.

scholarly journals A hybrid genetic algorithm for the traveling salesman problem with drone

2019 ◽  
Vol 26 (2) ◽  
pp. 219-247 ◽  
Author(s):  
Quang Minh Ha ◽  
Yves Deville ◽  
Quang Dung Pham ◽  
Minh Hoàng Hà
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Hybrid Genetic Algorithm ◽  
Traveling Salesman ◽  
The Traveling Salesman Problem

scholarly journals An enhanced hybrid genetic algorithm for solving traveling salesman problem

2020 ◽  
Vol 18 (2) ◽  
pp. 1035
Author(s):  
Zeravan Arif Ali ◽  
Subhi Ahmed Rasheed ◽  
Nabeel No’man Ali
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Search Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Mutation Operators ◽  
Np Hard Problem ◽  
The Traveling Salesman Problem

<span>Robust known the exceedingly famed NP-hard problem in combinatorial optimization is the Traveling Salesman Problem (TSP), promoting the skillful algorithms to get the solution of TSP have been the burden for several scholars. For inquiring global optimal solution, the presented algorithm hybridizes genetic and local search algorithm to take out the uplifted quality results. The genetic algorithm gives the best individual of population by enhancing both cross over and mutation operators while local search gives the best local solutions by testing all neighbor solution. By comparing with the conventional genetic algorithm, the numerical outcomes acts that the presented algorithm is more adequate to attain optimal or very near to it. Problems arrested from the TSP library strongly trial the algorithm and shows that the proposed algorithm can reap outcomes within reach optimal. For more details, please download TEMPLATE HELP FILE from the website.</span>

scholarly journals Evolutionary Divide and Conquer (I): A Novel Genetic Approach to the TSP

1993 ◽  
Vol 1 (4) ◽  
pp. 313-333 ◽  
Author(s):  
Christine L. Valenzuela ◽  
Antonia J. Jones
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Divide And Conquer ◽  
Search Problem ◽  
Genetic Approach ◽  
Solution Quality ◽  
Alternative Approach ◽  
The Traveling Salesman Problem

Experiments with genetic algorithms using permutation operators applied to the traveling salesman problem (TSP) tend to suggest that these algorithms fail in two respects when applied to very large problems: they scale rather poorly as the number of cities n increases, and the solution quality degrades rapidly. We propose an alternative approach for genetic algorithms applied to hard combinatoric search which we call Evolutionary Divide and Conquer (EDAC). This method has potential for any search problem in which knowledge of good solutions for subproblems can be exploited to improve the solution of the problem itself. The idea is to use the genetic algorithm to explore the space of problem subdivisions rather than the space of solutions themselves. We give some preliminary results of this method applied to the geometric TSP.

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