Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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In this work a memetic algorithm for the Bus Network Scheduling Problem (BNSP) is presented. The algorithm comprises two stages: the first one calculates the distance among all the pairs of bus stops, and the second one is a MOEA that uses a novel simulation procedure for the calculus of the fitness function. This simulation method was specially developed for the BNSP. The EA used for the second stage was selected between the IBEA, NSGA-II and SPEA2 by means of some PISA tools. As a result of this experimentation, the SPEA2 was preferred since it presents the more spread solution set.