Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Software project management with GAs
Information Sciences: an International Journal
Optimal antenna placement using a new multi-objective chc algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Heterogeneous computing scheduling with evolutionary algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Using multi-objective metaheuristics to solve the software project scheduling problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A parallel micro evolutionary algorithm for heterogeneous computing and grid scheduling
Applied Soft Computing
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In this article we analyze the behavior and scalability of the CHC algorithm over a benchmark of instances of the software project scheduling problem. Our goal is to analyze the performance of the CHC algorithm when solving realistic NP-hard combinatorial problems and test whether its previously reported high performance on similar problems also holds on this one. We perform a preliminary study to obtain a suitable configuration of the parameters in the algorithm. After choosing the configuration, we show the results for the problem instances in the benchmark. To give a reference on how CHC performs and scales, its results are compared against those of a GA. We conclude that CHC outperforms GA in large problem instances. Moreover, CHC produces promising results for the software project scheduling problem domain, and could be used by practitioners.