Abstract
Macroevolutionary Algorithms seem to work better than other Evolutionary Algorithms in problems characterized by having small populations where the evaluation of the individuals is computationally very expensive or is characterized by a very difficult search space with multiple narrow hyper-dimensional peaks and large areas between those peaks showing the same fitness value. This paper focuses on some aspects of Macroevolutionary Algorithms introducing some modifications that address weak points in the original algorithm, which are very relevant in some types of complex real world problems. All the modifications on the algorithm are tested in real world problems.
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© 2007 Springer-Verlag Berlin Heidelberg
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Becerra, J.A., Casás, V.D., Duro, R.J. (2007). Exploring Macroevolutionary Algorithms: Some Extensions and Improvements. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_38
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DOI: https://doi.org/10.1007/978-3-540-73007-1_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73006-4
Online ISBN: 978-3-540-73007-1
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