- IUMSP, Lausanne, SwitzerlandPostdoc in statistical genetics, 2014 - present
- University College Dublin - National University of Ireland, DublinPhD, 2009 - 2014
Now, at long last, is this the time when the politicians will finally accept that more assertive measures are needed? Unfortunately, I'm not counting on it. Yes, there's obviously a problem - but we'll still get a few more years of politicking over how to fix it.
Sony Kapoor writes:
"While this [buying loads of SME loans directly from banks] goes beyond conventional QE, it may be necessary in the euro zone’s bank dominated system."
i.e. give money to people who will spend it (not banks).
"... perhaps the most determinant for successful QE will actually be a looser fiscal stance."
i.e. governments should be required to run a larger deficit, not a smaller one. This is probably the best way to do it, there's only so much the central banks can do fiddling with interest rates. And if national governments won't do the right thing, shouldn't the EU itself directly tax/spend/borrow on its own account?
A less than 10 min talk how utf-8 works. A must see for every utf-8 user, that means every user of the internet, that means you.
... The innovation strategy should be simplified, it says, “with a drastic reduction in the number of government agencies involved in funding innovation” – 11 at last count. It suggests a small number of agencies would be sufficient, with one dealing with applied research and innovation, and another with science and basic research. A high level coordination committee is also needed, it says, to prevent gaps or duplication. All this, it says, would allow a better focus on strengthening links between the business and academic communities, it adds.
It also calls for the development of applied research centres. The Paris based organisation agrees with the strategy of focusing on attracting high-tech multinationals, but adds that there could be more “spillover” between such companies and domestic SMEs – a sort of knowlegde, skills and overall innovation transfer. Firms involvement in patenting intellectual properties is below the average of 15 other OECD countries in nearly all industries, it points out.
To elaborate on the above - I think there’s a tendency in the GA research community (and perhaps this generalizes to researchers who specialize in a particular technique, be it within ML, optimization, or otherwise) to miss out on the bigger picture of where their research fits in with research in the wider field. It’s easy to get caught up researching how and why the algorithm works (and doesn’t work), or in optimizing a GA for an application area/type, and to forget to context in which this work sits - i.e., what types of problems do GAs do a good job at solving. Also, there isn’t enough benchmarking carried out against other techniques - this is maybe one place where we as GA researchers lose credibility.
Having said that, I’m not sure that GP is only suitable for solving dynamic optimization problems. I’ve ended up working on dynamic problems (and how to induce robustness more generally) partly by accident. I’m not the best person to conclude anything that applies generally about the utility of GP/GAs outside of those problem-types (i.e. noisy/dynamic problems). But I agree that the GA research community as a whole should focus on demonstrating results across a variety of problem-types, and show how they stack up against alternative approaches, and move the focus away from evangelizing about the technique.
“To be truly dynamic might mean that selection and evolution continues in the dynamic environment. i.e. that we are not using a single individual, or a static population of individuals, to try to drive our decisions. I take it this is what you mean? “
Yes, that’s what I mean. People use “random immigrants” (randomly generated individuals, introduced into the population, replacing existing individuals) when they detect that the environment has changed. Another approach involves using a constantly updated memory of solutions that have done well in the past (suitability depends on the characterization of the environment).
“You want it to be the case that certain "patterns" in the market data appear again and again, allowing the relevant robust "gene" to become useful again in the future.”
I haven’t looked at this at the level of genes - we don’t really work with genes in GP (the genotype and phenotype are the same in tree-based GP) - the closest analogue is a subtree (isolated solution component). (John Mark’s thesis was largely concerned with modularity - that is - isolating useful subtrees to be used when building solutions, but afaik he didn't look at dynamic environments).
There have been studies that have used a memory of individuals that have performed well in the past. This approach is suitable in environments where there is a high likelihood that conditions similar or identical to those seen in the past, will be seen again in the future (e.g. your Christmas example :-)
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