It is Time for New Perspectives on How to Fight Bloat in GP

The present and future of evolutionary algorithms depends on the proper use of modern parallel and distributed computing infrastructures. Although still sequential approaches dominate the landscape, available multi-core, many-core and distributed systems will make users and researchers to more frequently deploy parallel version of the algorithms. In such a scenario, new possibilities arise regarding the time saved when parallel evaluation of individuals are performed. And this time saving is particularly relevant in Genetic Programming. This paper studies how evaluation time influences not only time to solution in parallel/distributed systems, but may also affect size evolution of individuals in the population, and eventually will reduce the bloat phenomenon GP features. This paper considers time and space as two sides of a single coin when devising a more natural method for fighting bloat. This new perspective allows us to understand that new methods for bloat control can be derived, and the first of such a method is described and tested. Experimental data confirms the strength of the approach: using computing time as a measure of individuals' complexity allows to control the growth in size of genetic programming individuals

Evolving DNA motifs to predict GeneChip probe performance

Background: Affymetrix High Density Oligonuclotide Arrays (HDONA) simultaneously measure expression of thousands of genes using millions of probes. We use correlations between measurements for the same gene across 6685 human tissue samples from NCBI's GEO database to indicated the quality of individual HG-U133A probes. Low correlation indicates a poor probe. Results: Regular expressions can be automatically created from a Backus-Naur form (BNF) context-free grammar using strongly typed genetic programming. Conclusion: The automatically produced motif is better at predicting poor DNA sequences than an existing human generated RE, suggesting runs of Cytosine and Guanine and mixtures should all be avoided. © 2009 Langdon and Harrison; licensee BioMed Central Ltd

Moving carbon between spheres, the potential oxalate-carbonate pathway of Brosimum alicastrum Sw.; Moraceae.

Aims The Oxalate-Carbonate Pathway (OCP) is a biogeochemical process that transfers atmospheric CO2 into the geologic reservoir as CaCO3; however, until now all investigations on this process have focused on species with limited food benefits. This study evaluates a potential OCP associated with Brosimum alicastrum, a Neotropical species with agroforestry potential (ca. 70–200 kg-nuts yr−1), in the calcareous soils of Haiti and Mexico. Methods / results Enzymatic analysis demonstrated significant concentrations of calcium oxalate (5.97 % D.W.) were associated with B. alicastrum tissue in all sample sites. The presence of oxalotrophism was also confirmed with microbiological analyses in both countries. High concentrations of total calcium (>7 g kg−1) and lithogenic carbonate obscured the localised alkalinisation and identification of secondary carbonate associated with the OCP at most sample sites, except Ma Rouge, Haiti. Soils adjacent to subjects in Ma Rouge demonstrated an increase in pH (0.63) and CaCO3 concentration (5.9 %) that, when coupled with root-like secondary carbonate deposits in Mexico, implies that the OCP does also occur in calcareous soils. Conclusions Therefore this study confirms that the OCP also occurs in calcareous soils, adjacent to B. alicastrum, and could play a fundamental and un-accounted role in the global calcium-carbon coupled cycle

Short-term fatty acid intervention elicits differential gene expression responses in adipose tissue from lean and overweight men

The goal of this study was to investigate the effect of a short-term nutritional intervention on gene expression in adipose tissue from lean and overweight subjects. Gene expression profiles were measured after consumption of an intervention spread (increased levels of polyunsaturated fatty acids, conjugated linoleic acid and medium chain triglycerides) and a control spread (40 g of fat daily) for 9 days. Adipose tissue gene expression profiles of lean and overweight subjects were distinctly different, mainly with respect to defense response and metabolism. The intervention resulted in lower expression of genes related to energy metabolism in lean subjects, whereas expression of inflammatory genes was down-regulated and expression of lipid metabolism genes was up-regulated in the majority of overweight subjects. Individual responses in overweight subjects were variable and these correlated better to waist–hip ratio and fat percentage than BMI

GOMGE: Gene-Pool Optimal Mixing on Grammatical Evolution

4noGene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a recent Evolutionary Algorithm (EA) in which the interactions among parts of the solution (i.e., the linkage) are learned and exploited in a novel variation operator. We present GOMGE, the extension of GOMEA to Grammatical Evolution (GE), a popular EA based on an indirect representation which may be applied to any problem whose solutions can be described using a context-free grammar (CFG). GE is a general approach that does not require the user to tune the internals of the EA to fit the problem at hand: there is hence the opportunity for benefiting from the potential of GOMEA to automatically learn and exploit the linkage. We apply the proposed approach to three variants of GE differing in the representation (original GE, SGE, and WHGE) and incorporate in GOMGE two specific improvements aimed at coping with the high degeneracy of those representations. We experimentally assess GOMGE and show that, when coupled with WHGE and SGE, it is clearly beneficial to both effectiveness and efficiency, whereas it delivers mixed results with the original GE.partially_openembargoed_20190822Medvet, Eric; Bartoli, Alberto; De Lorenzo, Andrea; Tarlao, FabianoMedvet, Eric; Bartoli, Alberto; De Lorenzo, Andrea; Tarlao, Fabian