Solución de problemas tipo Flow-Shop mediante algoritmos evolutivos
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Rodríguez Quiñones, Tania Andrea
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En esta Tesis se presenta una modificación al algoritmo genético HaEa (Hybrid Adaptative Evolutionary Algorithm) para resolver problemas de secuenciamiento tipo Flow-Shop. Se revisan tres operadores genéticos de mutación y tres de cruce, los cuáles han sido utilizados en diferentes soluciones a problemas de permutación. Se utiliza la heurística NEH para generar un individuo de la población inicial logrando obtener buenas soluciones. Los resultados experimentales muestran que entre los mejores operadores genéticos están la mutación de corrimiento y el cruce por emparejamiento parcial(PMX).
Abstract. In this thesis a modification to Hybrid Adaptative Evolutionary Algorithm (HaEa), is presented to solve Flow-Shop schedulling problems. Three dfferent mutations and three crossovers genetic operators are reviewed which have been used in different solutions for permutation problems. NEH heuristic is used to generate an individual of the initial population being able to obtain solutions in neighborhood on optimal point. The experimental results show that Shift Mutation and Partially Mapped Crossover (PMX) are among the best genetic operators
Abstract. In this thesis a modification to Hybrid Adaptative Evolutionary Algorithm (HaEa), is presented to solve Flow-Shop schedulling problems. Three dfferent mutations and three crossovers genetic operators are reviewed which have been used in different solutions for permutation problems. NEH heuristic is used to generate an individual of the initial population being able to obtain solutions in neighborhood on optimal point. The experimental results show that Shift Mutation and Partially Mapped Crossover (PMX) are among the best genetic operators