A rigorous evaluation of crossover and mutation in genetic programming

The role of crossover and mutation in Genetic Programming (GP) has been the subject of much debate since the emergence of the field. In this paper, we contribute new empirical evidence to this argument using a rigorous and principled experimental method applied to six problems common in the GP literature. The approach tunes the algorithm parameters to enable a fair and objective comparison of two different GP algorithms, the first using a combination of crossover and reproduction, and secondly using a combination of mutation and reproduction. We find that crossover does not significantly outperform mutation on most of the problems examined. In addition, we demonstrate that the use of a straightforward Design of Experiments methodology is effective at tuning GP algorithm parameters

Adaption of Operator Probabilities in Genetic Programming

Abstract. In this work we tried to reduce the number of free parameters within Genetic Programming without reducing the quality of the results. We developed three new methods to adapt the probabilities, different genetic operators are applied with. Using two problems from the areas of symbolic regression and classification we showed that the results in these cases were better than randomly chosen parameter sets and could compete with parameter sets chosen with empirical knowledge.

Memory with memory in genetic programming

We introduce Memory with Memory Genetic Programming (MwM-GP), where we use soft assignments and soft return operations. Instead of having the new value completely overwrite the old value of registers or memory, soft assignments combine such values. Similarly, in soft return operations the value of a function node is a blend between the result of a calculation and previously returned results. In extensive empirical tests, MwM-GP almost always does as well as traditional GP, while significantly outperforming it in several cases. MwM-GP also tends to be far more consistent than traditional GP. The data suggest that MwM-GP works by successively refining an approximate solution to the target problem and that it is much less likely to have truly ineffective code. MwM-GP can continue to improve over time, but it is less likely to get the sort of exact solution that one might find with traditional GP

The dusty SF history of high-z galaxies, modelling tools and future prospects

We summarize recent advances in the determination of the cosmic history of star formation and other properties of high-z galaxies, and the relevance of this information in our understanding of the formation of structures. We emphasize the importance of dust reprocessing in the high--z universe, as demonstrated in particular by IR and sub-mm data. This demand a panchromatic approach to observations and suitable modelling tools. We spend also some words on expectations from future instruments.Comment: 8 pages, to be published in "The link between stars and cosmology", 26-30 March, 2001, Puerto Vallarta, Mexico, by Kluwer, eds. M. Chavez, A. Bressan, A. Buzzoni, and D. Mayy