Optimization Problems And Genetic Algorithms
Jozef Zurada
- 17 Dec 2010
Vol. 14, Iss: 3
TL;DR: Using the techniques of selection, crossover, and mutation borrowed from the Darwin’s evolution theory, GAs were able to find the optimal solution after generating only 24 populations of solutions instead of exploring more than a million possible solutions.
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Abstract: This paper presents an application of genetic algorithms (GAs) to a well-known traveling salesman problem (TSP) which is a challenging optimization task. Using the techniques of selection, crossover, and mutation borrowed from the Darwin’s evolution theory, GAs were able to find the optimal solution after generating only 24 populations of solutions instead of exploring more than a million possible solutions.
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Citations
Using particle swarm optimization to solve test functions problems
TL;DR: The benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm and it is compared with genetic algorithm (GA) in order to prove capability of PSO.
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